5.3.9.3 Total Variation Restoration, TV Restoration

Chapter Contents (Back)
Noise Removal. Total Variation. Variational Reconstruction. Denoising.
See also Split Bregman Motion Blur, Image Restoration.

Vogel, C.R., Oman, M.E.,
Iterative methods for total variation denoising,
SIAM_JSC(17), No. 1, 1986, pp. 227-238. BibRef 8600

Li, Y.Y., Santosa, F.,
A Computational Algorithm for Minimizing Total Variation in Image Restoration,
IP(5), No. 6, June 1996, pp. 987-995.
IEEE DOI 9607
BibRef

Chan, T.F., Wong, C.K.,
Total Variation Blind Deconvolution,
IP(7), No. 3, March 1998, pp. 370-375.
IEEE DOI 9803
BibRef

Hampson, F.J., Pesquet, J.C.,
M-band Nonlinear Subband Decompositions with Perfect Reconstruction,
IP(7), No. 11, November 1998, pp. 1547-1560.
IEEE DOI BibRef 9811

Combettes, P.L., Pesquet, J.C.,
Image Restoration Subject to a Total Variation Constraint,
IP(13), No. 9, September 2004, pp. 1213-1222.
IEEE DOI 0409
BibRef
Earlier:
Incorporating total variation information in image recovery,
ICIP03(III: 373-376).
IEEE DOI 0312
BibRef

Combettes, P.L., Pesquet, J.C.,
Estimating first-order finite-difference information in image restoration problems,
ICIP04(I: 321-324).
IEEE DOI 0505
BibRef

Bolte, J., Combettes, P.L., Pesquet, J.C.,
Alternating proximal algorithm for blind image recovery,
ICIP10(1673-1676).
IEEE DOI 1009
BibRef

Combettes, P.L., Condat, L., Pesquet, J.C., Vu, B.C.,
A forward-backward view of some primal-dual optimization methods in image recovery,
ICIP14(4141-4145)
IEEE DOI 1502
Decision support systems BibRef

Chan, T.F.[Tony F.], Kang, S.H.[Sung Ha], Shen, J.H.[Jian-Hong],
Total Variation Denoising and Enhancement of Color Images Based on the CB and HSV Color Models,
JVCIR(12), No. 4, December 2001, pp. 422-435.
DOI Link 0204

See also Total Variation Wavelet Inpainting.
See also Image Denoising Using Mean Curvature of Image Surface. BibRef

Malgouyres, F.,
Minimizing the total variation under a general convex constraint for image restoration,
IP(11), No. 12, December 2002, pp. 1450-1456.
IEEE DOI 0301
BibRef
Earlier:
Total variation based oversampling of noisy images,
ScaleSpace01(xx-yy). 0106
BibRef
Earlier:
Combining Total Variation and Wavelet Packet Approaches for Image Deblurring,
LevelSet01(xx-yy). 0106
BibRef

Malgouyres, F.,
Image Compression Through a Projection onto a Polyhedral Set,
JMIV(27), No. 2, February 2007, pp. 193-200.
Springer DOI 0704
BibRef

Chambolle, A.[Antonin],
An Algorithm for Total Variation Minimization and Applications,
JMIV(20), No. 1-2, January-March 2004, pp. 89-97.
DOI Link 0403
BibRef
And:
Total Variation Minimization and a Class of Binary MRF Models,
EMMCVPR05(136-152).
Springer DOI 0601
BibRef
Earlier:
Partial differential equations and image processing,
ICIP94(I: 16-20).
IEEE DOI 9411
Applications to image denoising, zooming, and the computation of the mean curvature motion of interfaces.
See also TV-L1 Optical Flow Estimation. Second implementation:
See also Chambolle's Projection Algorithm for Total Variation Denoising. BibRef

Chambolle, A.[Antonin], Pock, T.[Thomas],
Learning Consistent Discretizations of the Total Variation,
SIIMS(14), No. 2, 2021, pp. 778-813.
DOI Link 2107
BibRef

Jalalzai, K.[Khalid], Chambolle, A.[Antonin],
Enhancement of Blurred and Noisy Images Based on an Original Variant of the Total Variation,
SSVM09(368-376).
Springer DOI 0906
BibRef

Jalalzai, K.[Khalid],
Some Remarks on the Staircasing Phenomenon in Total Variation-Based Image Denoising,
JMIV(54), No. 2, February 2016, pp. 256-268.
WWW Link. 1602
BibRef

Aujol, J.F.[Jean-Francois], Kang, S.H.[Sung Ha],
Color image decomposition and restoration,
JVCIR(17), No. 4, August 2006, pp. 916-928.
Elsevier DOI 0711
Total variation; Structure; Texture; Color; Image decomposition; Image restoration BibRef

Aujol, J.F.[Jean-François], Gilboa, G.[Guy],
Constrained and SNR-Based Solutions for TV-Hilbert Space Image Denoising,
JMIV(26), No. 1-2, November 2006, pp. 217-237.
Springer DOI 0701
BibRef

Aujol, J.F.[Jean-François],
Some First-Order Algorithms for Total Variation Based Image Restoration,
JMIV(34), No. 3, July 2009, pp. xx-yy.
Springer DOI 0906
BibRef

Duval, V.[Vincent], Aujol, J.F.[Jean-François], Gousseau, Y.[Yann],
A Bias-Variance Approach for the Nonlocal Means,
SIIMS(4), No. 2, 2011, pp. 760-788.
WWW Link. 1110
BibRef

Shi, H.[Hui], Traonmilin, Y.[Yann], Aujol, J.F.[Jean-François],
Sketched Learning for Image Denoising,
SSVM21(281-293).
Springer DOI 2106
BibRef

Lysaker, M.[Marius], Tai, X.C.[Xue-Cheng],
Iterative Image Restoration Combining Total Variation Minimization and a Second-Order Functional,
IJCV(66), No. 1, January 2006, pp. 5-18.
Springer DOI 0601
BibRef

Marquina, A.[Antonio],
Nonlinear Inverse Scale Space Methods For Total Variation Blind Deconvolution,
SIIMS(2), No. 1, 2009, pp. 64-83. total variation restoration; blind deconvolution; Gaussian blur; denoising; inverse scale space methods
DOI Link BibRef 0900

Malgouyres, F., Zeng, T.,
A Predual Proximal Point Algorithm Solving a Non Negative Basis Pursuit Denoising Model,
IJCV(83), No. 3, July 2009, pp. xx-yy.
Springer DOI 0904
BibRef

Landi, G.,
A Truncated Lagrange Method for Total Variation-Based Image Restoration,
JMIV(27), No. 2, June 2007, pp. 113-123.
Springer DOI 0710
BibRef

Landi, G.,
A Modified Newton Projection Method for TeX -Regularized Least Squares Image Deblurring,
JMIV(51), No. 1, January 2015, pp. 195-208.
WWW Link. 1503
BibRef

Landi, G., Piccolomini, E.L.[E. Loli],
An Algorithm for Image Denoising with Automatic Noise Estimate,
JMIV(34), No. 1, May 2009, pp. xx-yy.
Springer DOI 0905
BibRef

Chartrand, R., Staneva, V.,
Total variation regularisation of images corrupted by non-Gaussian noise using a quasi-Newton method,
IET-IPR(2), No. 6, December 2008, pp. 295-303.
DOI Link 0905
BibRef

Li, F.[Fang], Shen, C.M.[Chao-Min], Fan, J.S.[Jing-Song], Shen, C.L.[Chun-Li],
Image restoration combining a total variational filter and a fourth-order filter,
JVCIR(18), No. 4, August 2007, pp. 322-330.
Elsevier DOI 0711
Image restoration; Total variation; Fourth-order filter; BV space; BV2 space BibRef

Li, F.[Fang], Shen, C.M.[Chao-Min], Shen, C.L.[Chun-Li], Zhang, G.X.[Gui-Xu],
Variational denoising of partly textured images,
JVCIR(20), No. 4, May 2009, pp. 293-300.
Elsevier DOI 0905
Variational denoising; Total variation; Texture detecting function; Local feature BibRef

Ng, M.K.[Michael K.], Qi, L.Q.[Li-Qun], Yang, Y.F.[Yu-Fei], Huang, Y.M.[Yu-Mei],
On Semismooth Newton's Methods for Total Variation Minimization,
JMIV(27), No. 3, April 2007, pp. 265-276.
Springer DOI 0704
BibRef

Yu, G.H.[Gao-Hang], Qi, L.Q.[Li-Qun], Dai, Y.H.[Yu-Hong],
On Nonmonotone Chambolle Gradient Projection Algorithms for Total Variation Image Restoration,
JMIV(35), No. 2, October 2009, pp. xx-yy.
Springer DOI 0907
BibRef

Beck, A., Teboulle, M.,
Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems,
IP(18), No. 11, November 2009, pp. 2419-2434.
IEEE DOI 0911
BibRef

Wang, Y.L.[Yi-Lun], Yang, J.F.[Jun-Feng], Yin, W.T.[Wo-Tao], Zhang, Y.[Yin],
A New Alternating Minimization Algorithm For Total Variation Image Reconstruction,
SIIMS(1), No. 3, 2008, pp. 248-272.
DOI Link half-quadratic; image deblurring; isotropic total variation; fast Fourier transform BibRef 0800

Yang, J.F.[Jun-Feng], Yin, W.T.[Wo-Tao], Zhang, Y.[Yin], Wang, Y.L.[Yi-Lun],
A Fast Algorithm For Edge-Preserving Variational Multichannel Image Restoration,
SIIMS(2), No. 2, 2009, pp. 569-592.
DOI Link half-quadratic; cross-channel; image deblurring; total variation; fast Fourier transform BibRef 0900

El Hamidi, A., Menard, M., Lugiez, M., Ghannam, C.,
Weighted and extended total variation for image restoration and decomposition,
PR(43), No. 4, April 2010, pp. 1564-1576.
Elsevier DOI 1002
Convex and non-convex regularization; Texture decomposition; Chambolle's projection; Weighted total variation; Extended total variation BibRef

El Hamidi, A., Ghannam, C., Bailly-Maitre, G., Menard, M.,
Nonstandard diffusion in image restoration and decomposition,
ICIP09(3945-3948).
IEEE DOI 0911
BibRef

Dong, Y.Q.[Yi-Qiu], Hintermuller, M.[Michael], Neri, M.[Marrick],
An Efficient Primal-Dual Method For L_1 TV Image Restoration,
SIIMS(2), No. 4, 2009, pp. 1168-1189.
DOI Link 1002
deblurring; duality; L1-data fitting; random-valued impulse noise; salt-and-pepper noise; semismooth Newton; total variation regularization BibRef

Dong, Y.Q.[Yi-Qiu], Hintermüller, M.[Michael], Rincon-Camacho, M.M.[M. Monserrat],
Automated Regularization Parameter Selection in Multi-Scale Total Variation Models for Image Restoration,
JMIV(40), No. 1, May 2011, pp. 82-104.
WWW Link. 1103
BibRef
Earlier: A1, A2, Only:
Multi-scale Total Variation with Automated Regularization Parameter Selection for Color Image Restoration,
SSVM09(271-281).
Springer DOI 0906
BibRef

Dong, Y.Q.[Yi-Qiu], Hintermüller, M.[Michael], Rincon-Camacho, M.M.[M. Monserrat],
A Multi-Scale Vectorial L-tau-TV Framework for Color Image Restoration,
IJCV(92), No. 3, May 2011, pp. 296-307.
WWW Link. 1103

See also Multiphase Image Segmentation and Modulation Recovery Based on Shape and Topological Sensitivity.
See also Inexact Newton-CG-Type Active Contour Approach for the Minimization of the Mumford-Shah Functional, An. BibRef

Chen, Q.A.[Qi-Ang], Montesinos, P.[Philippe], Sun, Q.S.[Quan Sen], Heng, P.A.[Peng Ann], Xia, D.S.[De Shen],
Adaptive total variation denoising based on difference curvature,
IVC(28), No. 3, March 2010, pp. 298-306.
Elsevier DOI 1001
Image denoise; Total variation; Difference curvature; Staircase effect; Loss of details
See also double-threshold image binarization method based on edge detector, A.
See also Parametric active contours for object tracking based on matching degree image of object contour points. BibRef

Chen, D.Q.[Dai-Qiang], Cheng, L.Z.[Li-Zhi],
Alternative minimisation algorithm for non-local total variational image deblurring,
IET-IPR(4), No. 5, October 2010, pp. 353-364.
DOI Link 1011
BibRef

Pang, Z.F.[Zhi-Feng], Yang, Y.F.[Yu-Fei],
A projected gradient algorithm based on the augmented Lagrangian strategy for image restoration and texture extraction,
IVC(29), No. 2-3, February 2011, pp. 117-126.
Elsevier DOI 1101
Augmented Lagrangian strategy; Image restoration; Texture extraction; Projected gradient method; Total variation; High-order PDEs Mixed model which combines the Rudin-Osher-Fatemi (ROF) model
See also Nonlinear total variation based noise removal algorithms. with the Lysaker-Lundevold-Tai (LLT) model to reduce the staircase effect and blur. BibRef

Wu, J.[Jian], Tang, C.[Chen],
An efficient decision-based and edge-preserving method for salt-and-pepper noise removal,
PRL(32), No. 15, 1 November 2011, pp. 1974-1981.
Elsevier DOI 1112
Image denoising; Impulse noise; Edge-preservation; Total variation inpainting; The two-stage scheme BibRef

Wu, J.[Jian], Tang, C.[Chen],
Random-valued impulse noise removal using fuzzy weighted non-local means,
SIViP(8), No. 2, February 2014, pp. 349-355.
WWW Link. 1402
BibRef

Shaked, E., Michailovich, O.V.[Oleg V.],
Iterative Shrinkage Approach to Restoration of Optical Imagery,
IP(20), No. 2, February 2011, pp. 405-416.
IEEE DOI 1102
Poisson noise. BibRef

Michailovich, O.V.[Oleg V.],
An Iterative Shrinkage Approach to Total-Variation Image Restoration,
IP(20), No. 5, May 2011, pp. 1281-1299.
IEEE DOI 1104
BibRef

Hao, B.B.[Bin-Bin], Zhu, J.G.[Jian-Guang],
Combining Total Variation and Nonlocal Means Regularization for Edge Preserving Image Deconvolution,
ELCVIA(10), No. 1, 2011, pp. -.
DOI Link 1112
BibRef

Bras, N.B., Bioucas-Dias, J.M., Martins, R.C., Serra, A.C.,
An Alternating Direction Algorithm for Total Variation Reconstruction of Distributed Parameters,
IP(21), No. 6, June 2012, pp. 3004-3016.
IEEE DOI 1202
BibRef

Jin, Y.[Yan], Jost, J.[Jürgen], Wang, G.F.[Guo-Fang],
A Nonlocal Version of the Osher-Solé-Vese Model,
JMIV(44), No. 2, October 2012, pp. 99-113.
WWW Link. 1206
Total Variation (TV) model, Nonlocal means, Denoising, OSV model, Nonlocal OSV model, Nonlocal TV model BibRef

Jin, Y.[Yan], Jost, J.[Jürgen], Wang, G.F.[Guo-Fang],
A New Nonlocal H 1 Model for Image Denoising,
JMIV(48), No. 1, January 2014, pp. 93-105.
WWW Link. 1402
BibRef

Bonettini, S.[Silvia], Ruggiero, V.[Valeria],
On the Convergence of Primal-Dual Hybrid Gradient Algorithms for Total Variation Image Restoration,
JMIV(44), No. 3, November 2012, pp. 236-253.
WWW Link. 1209
BibRef

Fei, X.[Xuan], Wei, Z.H.[Zhi-Hui], Xiao, L.[Liang],
Iterative Directional Total Variation Refinement for Compressive Sensing Image Reconstruction,
SPLetters(20), No. 11, 2013, pp. 1070-1073.
IEEE DOI 1310
compressed sensing BibRef

Zhang, Z.[Zhen], Shi, Y.H.[Yun-Hui], Ding, W.P.[Wen-Peng], Yin, B.C.[Bao-Cai],
MR images reconstruction based on TVWL2-L1 model,
JVCIR(24), No. 2, February 2013, pp. 187-195.
Elsevier DOI 1302
Compressive sensing; MR image reconstruction; Convex optimization; Wavelet transform; Total variation BibRef

Zhang, Z.[Zhen], Shi, Y.H.[Yun-Hui], Yin, B.C.[Bao-Cai],
Compressive sensing image recovery based on equalization quantization noise model,
VCIP11(1-4).
IEEE DOI 1201
BibRef

Fei, X.[Xuan], Li, L.[Lei], Cao, H.L.[He-Ling], Miao, J.Y.[Jian-Yu], Yu, R.P.[Ren-Ping],
View's dependency and low-rank background-guided compressed sensing for multi-view image joint reconstruction,
IET-IPR(13), No. 12, October 2019, pp. 2294-2303.
DOI Link 1911
BibRef

Lou, J.T.[Jing-Tao], Li, Y.L.[Yong-Le], Liu, Y.[Yu], Tan, S.[Shuren], Zhang, M.J.[Mao-Jun],
Omni-gradient-based total variation minimisation for sparse reconstruction of omni-directional image,
IET-IPR(8), No. 7, July 2014, pp. 397-405.
DOI Link 1408
compressed sensing BibRef

Liu, Y.P.[Yi-Peng], Wu, S.[Shan], Huang, X.L.[Xiao-Lin], Chen, B.[Bing], Zhu, C.[Ce],
Hybrid CS-DMRI: Periodic Time-Variant Subsampling and Omnidirectional Total Variation Based Reconstruction,
MedImg(36), No. 10, October 2017, pp. 2148-2159.
IEEE DOI 1710
Correlation, Current measurement, Image reconstruction, Image restoration, Matrix converters, Optimization, TV, Compressive sensing, image acquisition, image reconstruction, magnetic, resonance, imaging BibRef

Liu, Y.P.[Yi-Peng], Long, Z.[Zhen], Zhu, C.[Ce],
Image Completion Using Low Tensor Tree Rank and Total Variation Minimization,
MultMed(21), No. 2, February 2019, pp. 338-350.
IEEE DOI 1902
Tensile stress, TV, Minimization, Optimization, Matrix decomposition, Data structures, Color, Tensor tree decomposition, low rank tensor approximation BibRef

Oh, S.M.[Seung-Mi], Woo, H.[Hyenkyun], Yun, S.W.[Sang-Woon], Kang, M.J.[Myung-Joo],
Non-convex hybrid total variation for image denoising,
JVCIR(24), No. 3, April 2013, pp. 332-344.
Elsevier DOI 1303
Non-convex TV; Non-convex HOTV; Non-convex hybrid TV; Iterative reweighted algorithm; Image denoising; Total Variation (TV); The alternating direction method of multiplier (ADMM); Staircase artifacts BibRef

Chan, R.H.[Raymond H.], Tao, M.[Min], Yuan, X.M.[Xiao-Ming],
Constrained Total Variation Deblurring Models and Fast Algorithms Based on Alternating Direction Method of Multipliers,
SIIMS(6), No. 1, 2013, pp. 680-697.
DOI Link 1304
BibRef

Needell, D.[Deanna], Ward, R.[Rachel],
Stable Image Reconstruction Using Total Variation Minimization,
SIIMS(6), No. 2, 2013, pp. 1035-1058.
DOI Link 1307
BibRef

Needell, D.[Deanna], Ward, R.[Rachel],
Near-Optimal Compressed Sensing Guarantees for Total Variation Minimization,
IP(22), No. 10, 2013, pp. 3941-3949.
IEEE DOI 1309
L1-minimization BibRef

Condat, L.,
A Direct Algorithm for 1-D Total Variation Denoising,
SPLetters(20), No. 11, 2013, pp. 1054-1057.
IEEE DOI 1310
least squares approximations BibRef

Condat, L.[Laurent],
A Generic Proximal Algorithm for Convex Optimization: Application to Total Variation Minimization,
SPLetters(21), No. 8, August 2014, pp. 985-989.
IEEE DOI 1406
Convergence BibRef

Condat, L.[Laurent],
Discrete Total Variation: New Definition and Minimization,
SIIMS(10), No. 3, 2017, pp. 1258-1290.
DOI Link 1710
BibRef

Couprie, C., Grady, L., Najman, L., Pesquet, J., Talbot, H.,
Dual Constrained TV-based Regularization on Graphs,
SIIMS(6), No. 3, 2013, pp. 1246-1273.
DOI Link 1310
BibRef

Hintermüller, M.[Michael], Wu, T.[Tao],
Nonconvex TVq-Models in Image Restoration: Analysis and a Trust-Region Regularization-Based Superlinearly Convergent Solver,
SIIMS(6), No. 3, 2013, pp. 1385-1415.
DOI Link 1310
BibRef
Earlier:
A Smoothing Descent Method for Nonconvex TV q -Models,
Optimization11(119-133).
Springer DOI 1405
BibRef

Swoboda, P., Schnörr, C.,
Convex Variational Image Restoration with Histogram Priors,
SIIMS(6), No. 3, 2013, pp. 1719-1735.
DOI Link 1310
BibRef

Aghasi, A.[Alireza], Romberg, J.[Justin],
Sparse Shape Reconstruction,
SIIMS(6), No. 4, 2013, pp. 2075-2108.
DOI Link 1402
BibRef

Aghasi, A.[Alireza], Romberg, J.[Justin],
Convex Cardinal Shape Composition,
SIIMS(8), No. 4, 2015, pp. 2887-2950.
DOI Link 1601
BibRef

Aghasi, A.[Alireza], Romberg, J.[Justin],
Extracting the Principal Shape Components via Convex Programming,
IP(27), No. 7, July 2018, pp. 3513-3528.
IEEE DOI 1805
Character recognition, Dictionaries, Image reconstruction, Image segmentation, Optical character recognition software, nonlinear sparse recovery BibRef

Lee, Y.J.[Yeon Ju], Lee, S.[Sukho], Yoon, J.[Jungho],
A Framework for Moving Least Squares Method with Total Variation Minimizing Regularization,
JMIV(48), No. 3, March 2014, pp. 566-582.
WWW Link. 1403
BibRef

Duran, J.[Joan], Coll, B.[Bartomeu], Sbert, C.[Catalina],
Chambolle's Projection Algorithm for Total Variation Denoising,
IPOL(2013), No. 2013, pp. 311-331.
DOI Link 1403
Code, Total Variation. Code, Denoising.
See also Algorithm for Total Variation Minimization and Applications, An.
See also Nonlocal Image and Movie Denoising. BibRef

Coll, B., Duran, J., Sbert, C.,
An algorithm for nonconvex functional minimization and applications to image restoration,
ICIP14(4547-4551)
IEEE DOI 1502
Image edge detection BibRef

Batard, T.[Thomas], Sochen, N.A.[Nir A.],
A Class of Generalized Laplacians on Vector Bundles Devoted to Multi-Channel Image Processing,
JMIV(48), No. 3, March 2014, pp. 517-543.
Springer DOI 1403
fibre bundles theory. BibRef

Sawatzky, A.[Alex],
Performance of First-Order Algorithms for TV Penalized Weighted Least-Squares Denoising Problem,
ICISP14(340-349).
Springer DOI 1406
BibRef

Hosseini, M.S.[Mahdi S.], Plataniotis, K.N.[Konstantinos N.],
High-Accuracy Total Variation With Application to Compressed Video Sensing,
IP(23), No. 9, September 2014, pp. 3869-3884.
IEEE DOI 1410
FIR filters BibRef

Hosseini, M.S.[Mahdi S.], Plataniotis, K.N.[Konstantinos N.],
Derivative Kernels: Numerics and Applications,
IP(26), No. 10, October 2017, pp. 4596-4611.
IEEE DOI 1708
FIR filters, approximation theory, differentiation, edge detection, interpolation, 2D MaxFlat kernels, Canny edge detection, FIR filter, discrete approximation, finite impulse response filter, generalized framework, higher-order tensors, image directional differentiation, image interpolation problem, numerical differentiation, Cutoff frequency, Finite impulse response filters, BibRef

Hosseini, M.S.[Mahdi S.], Plataniotis, K.N.[Konstantinos N.],
Finite Differences in Forward and Inverse Imaging Problems: MaxPol Design,
SIIMS(10), No. 4, 2017, pp. 1963-1996.
DOI Link 1801
BibRef

Selesnick, I.W., Parekh, A., Bayram, I.,
Convex 1-D Total Variation Denoising with Non-convex Regularization,
SPLetters(22), No. 2, February 2015, pp. 141-144.
IEEE DOI 1410
concave programming BibRef

Parekh, A., Selesnick, I.W.,
Convex Denoising using Non-Convex Tight Frame Regularization,
SPLetters(22), No. 10, October 2015, pp. 1786-1790.
IEEE DOI 1506
Computer vision BibRef

Parekh, A., Selesnick, I.W.,
Enhanced Low-Rank Matrix Approximation,
SPLetters(23), No. 4, April 2016, pp. 493-497.
IEEE DOI 1604
AWGN BibRef

Ding, Y.[Yin], Selesnick, I.W.,
Artifact-Free Wavelet Denoising: Non-convex Sparse Regularization, Convex Optimization,
SPLetters(22), No. 9, September 2015, pp. 1364-1368.
IEEE DOI 1503
optimisation BibRef

Holt, K.M.,
Total Nuclear Variation and Jacobian Extensions of Total Variation for Vector Fields,
IP(23), No. 9, September 2014, pp. 3975-3989.
IEEE DOI 1410
Jacobian matrices BibRef

Fraysse, A.[Aurelia], Rodet, T.[Thomas],
A Measure-Theoretic Variational Bayesian Algorithm for Large Dimensional Problems,
SIIMS(7), No. 4, 2014, pp. 2591-2622.
DOI Link 1402
BibRef

Zheng, Y.L.[Yu-Ling], Fraysse, A.[Aurelia], Rodet, T.[Thomas],
Efficient Variational Bayesian Approximation Method Based on Subspace Optimization,
IP(24), No. 2, February 2015, pp. 681-693.
IEEE DOI 1502
BibRef
Earlier: A1, A3, A2:
Fast variational Bayesian approaches applied to large dimensional problems,
ICIP13(479-483)
IEEE DOI 1402
Hilbert spaces. Approximation methods BibRef

Gilboa, G.[Guy],
A Total Variation Spectral Framework for Scale and Texture Analysis,
SIIMS(7), No. 4, 2014, pp. 1937-1961.
DOI Link 1402
BibRef
Earlier:
A Spectral Approach to Total Variation,
SSVM13(36-47).
Springer DOI 1305
BibRef

Horesh, D.[Dikla], Gilboa, G.[Guy],
Separation Surfaces in the Spectral TV Domain for Texture Decomposition,
IP(25), No. 9, September 2016, pp. 4260-4270.
IEEE DOI 1609
BibRef
Earlier:
Multiscale Texture Orientation Analysis Using Spectral Total-Variation Decomposition,
SSVM15(486-497).
Springer DOI 1506
image texture BibRef

Moeller, M., Brinkmann, E., Burger, M., Seybold, T.,
Color Bregman TV,
SIIMS(7), No. 4, 2014, pp. 2771-2806.
DOI Link 1412
BibRef

Seybold, T., Kuhn, F., Habigt, J., Hartenstein, M., Stechele, W.,
Automatic denoising parameter estimation using gradient histograms,
VCIP14(358-361)
IEEE DOI 1504
image denoising BibRef

Bergmann, R.[Ronny], Laus, F., Steidl, G., Weinmann, A.[Andreas],
Second Order Differences of Cyclic Data and Applications in Variational Denoising,
SIIMS(7), No. 4, 2014, pp. 2916-2953.
DOI Link 1412

See also Second-Order TV-Type Approach for Inpainting and Denoising Higher Dimensional Combined Cyclic and Vector Space Data, A. BibRef

Bergmann, R.[Ronny], Fitschen, J.H.[Jan Henrik], Persch, J.[Johannes], Steidl, G.[Gabriele],
Priors with Coupled First and Second Order Differences for Manifold-Valued Image Processing,
JMIV(60), No. 9, November 2018, pp. 1459-1481.
Springer DOI 1810
BibRef
Earlier:
Infimal Convolution Coupling of First and Second Order Differences on Manifold-Valued Images,
SSVM17(447-459).
Springer DOI 1706
BibRef

Weinmann, A.[Andreas], Demaret, L.[Laurent], Storath, M.[Martin],
Total Variation Regularization for Manifold-Valued Data,
SIIMS(7), No. 4, 2014, pp. 2226-2257.
DOI Link 1412
BibRef

Bredies, K., Holler, M., Storath, M., Weinmann, A.,
Total Generalized Variation for Manifold-Valued Data,
SIIMS(11), No. 3, 2018, pp. 1785-1848.
DOI Link 1810
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Gao, Y., Bredies, K.[Kristian],
Infimal Convolution of Oscillation Total Generalized Variation for the Recovery of Images with Structured Texture,
SIIMS(11), No. 3, 2018, pp. 2021-2063.
DOI Link 1810
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Weinmann, A.[Andreas], Demaret, L.[Laurent], Storath, M.[Martin],
Mumford-Shah and Potts Regularization for Manifold-Valued Data,
JMIV(55), No. 3, July 2016, pp. 428-445.
Springer DOI 1604
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He, C.[Chuan], Hu, C.H.[Chang-Hua], Zhang, W.[Wei], Shi, B.[Biao],
A Fast Adaptive Parameter Estimation for Total Variation Image Restoration,
IP(23), No. 12, December 2014, pp. 4954-4967.
IEEE DOI 1412
estimation theory BibRef

Holler, M., Kunisch, K.,
On Infimal Convolution of TV-Type Functionals and Applications to Video and Image Reconstruction,
SIIMS(7), No. 4, 2014, pp. 2258-2300.
DOI Link 1412
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Chierchia, G., Pustelnik, N., Pesquet-Popescu, B., Pesquet, J.C.,
A Nonlocal Structure Tensor-Based Approach for Multicomponent Image Recovery Problems,
IP(23), No. 12, December 2014, pp. 5531-5544.
IEEE DOI 1412
convex programming. Extended Nonlocal total variation. BibRef

Eason, D.T.[Duncan T.], Andrews, M.[Mark],
Total Variation Regularization via Continuation to Recover Compressed Hyperspectral Images,
IP(24), No. 1, January 2015, pp. 284-293.
IEEE DOI 1502
BibRef
Earlier:
Compressed hyperspectral image recovery via total variation regularization assuming linear mixing,
ICIP14(620-624)
IEEE DOI 1502
convergence of numerical methods. Compressed sensing BibRef

Jung, Y.M.[Yoon Mo], Yun, S.W.[Sang-Woon],
Impedance Imaging With First-Order TV Regularization,
MedImg(34), No. 1, January 2015, pp. 193-202.
IEEE DOI 1502
electric impedance imaging BibRef

Jiang, W.F.[Wen-Fei], Cui, H.B.[Heng-Bin], Zhang, F.[Fan], Rong, Y.C.[Yao-Cheng], Chen, Z.B.[Zhi-Bo],
Oriented total variation /1/2 regularization,
JVCIR(29), No. 1, 2015, pp. 125-137.
Elsevier DOI 1504
Total variation TV/1 and TV/2. BibRef

Poon, C.[Clarice],
On the Role of Total Variation in Compressed Sensing,
SIIMS(8), No. 1, 2015, pp. 682-720.
DOI Link 1504
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Bredies, K.[Kristian], Sun, H.P.[Hong Peng],
Preconditioned Douglas-Rachford Algorithms for TV- and TGV-Regularized Variational Imaging Problems,
JMIV(52), No. 3, July 2015, pp. 317-344.
WWW Link. 1506
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Lefkimmiatis, S.[Stamatios], Roussos, A.[Anastasios], Maragos, P.[Petros], Unser, M.[Michael],
Structure Tensor Total Variation,
SIIMS(8), No. 2, 2015, pp. 1090-1122.
DOI Link 1507
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Lefkimmiatis, S.[Stamatios], Roussos, A.[Anastasios], Unser, M.[Michael], Maragos, P.[Petros],
Convex Generalizations of Total Variation Based on the Structure Tensor with Applications to Inverse Problems,
SSVM13(48-60).
Springer DOI 1305
BibRef

Nguyen, D.D.[Duc Dung], Jeon, J.W.[Jae Wook],
Multiple-constraint variational framework and image restoration problems,
IET-IPR(9), No. 6, 2015, pp. 435-449.
DOI Link 1507
computer vision BibRef

Bögelein, V.[Verena], Duzaar, F.[Frank], Marcellini, P.[Paolo],
A Time Dependent Variational Approach to Image Restoration,
SIIMS(8), No. 2, 2015, pp. 968-1006.
DOI Link 1507
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Selesnick, I.W.,
Generalized Total Variation: Tying the Knots,
SPLetters(22), No. 11, November 2015, pp. 2009-2013.
IEEE DOI 1509
convex programming BibRef

Selesnick, I.W.,
Total Variation Denoising Via the Moreau Envelope,
SPLetters(24), No. 2, February 2017, pp. 216-220.
IEEE DOI 1702
AWGN BibRef

Lou, Y.F.[Yi-Fei], Zeng, T.Y.[Tie-Yong], Osher, S.[Stanley], Xin, J.[Jack],
A Weighted Difference of Anisotropic and Isotropic Total Variation Model for Image Processing,
SIIMS(8), No. 3, 2015, pp. 1798-1823.
DOI Link 1511
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Bui, K.[Kevin], Park, F.[Fredrick], Lou, Y.F.[Yi-Fei], Xin, J.[Jack],
A Weighted Difference of Anisotropic and Isotropic Total Variation for Relaxed Mumford-Shah Color and Multiphase Image Segmentation,
SIIMS(14), No. 3, 2021, pp. 1078-1113.
DOI Link 2108
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Park, F., Lou, Y., Xin, J.,
A weighted difference of anisotropic and isotropic total variation for relaxed Mumford-Shah image segmentation,
ICIP16(4314-4318)
IEEE DOI 1610
Heuristic algorithms BibRef

Prasath, V.B.S.[V.B. Surya], Vorotnikov, D., Pelapur, R., Jose, S., Seetharaman, G., Palaniappan, K.,
Multiscale Tikhonov-Total Variation Image Restoration Using Spatially Varying Edge Coherence Exponent,
IP(24), No. 12, December 2015, pp. 5220-5235.
IEEE DOI 1512
edge detection BibRef

Prasath, V.B.S., Pelapur, R., Seetharaman, G., Palaniappan, K.,
Multiscale Structure Tensor for Improved Feature Extraction and Image Regularization,
IP(28), No. 12, December 2019, pp. 6198-6210.
IEEE DOI 1909
Image edge detection, Eigenvalues and eigenfunctions, Noise measurement, Feature extraction, Detectors, anisotropic diffusion BibRef

Liu, X.,
Weighted total generalised variation scheme for image restoration,
IET-IPR(10), No. 1, 2016, pp. 80-88.
DOI Link 1601
convergence of numerical methods BibRef

Zhang, J.P.[Jian-Ping], Chen, K.[Ke],
A Total Fractional-Order Variation Model for Image Restoration with Nonhomogeneous Boundary Conditions and Its Numerical Solution,
SIIMS(8), No. 4, 2015, pp. 2487-2518.
DOI Link 1601
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Hintermüller, M.[Michael], Valkonen, T.[Tuomo], Wu, T.[Tao],
Limiting Aspects of Nonconvex TV-phi Models,
SIIMS(8), No. 4, 2015, pp. 2581-2621.
DOI Link 1601
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Papafitsoros, K.[Konstantinos], Valkonen, T.[Tuomo],
Asymptotic Behaviour of Total Generalised Variation,
SSVM15(702-714).
Springer DOI 1506
BibRef

Bappy, D.M., Jeon, I.,
Combination of hybrid median filter and total variation minimisation for medical X-ray image restoration,
IET-IPR(10), No. 4, 2016, pp. 261-271.
DOI Link 1604
diagnostic radiography BibRef

Duran, J.[Joan], Moeller, M.[Michael], Sbert, C.[Catalina], Cremers, D.[Daniel],
Collaborative Total Variation: A General Framework for Vectorial TV Models,
SIIMS(9), No. 1, 2016, pp. 116-151.
DOI Link 1604
BibRef
Earlier:
A Novel Framework for Nonlocal Vectorial Total Variation Based on L p,q,r-norms,
EMMCVPR15(141-154).
Springer DOI 1504
BibRef

Möllenhoff, T.[Thomas], Strekalovskiy, E.[Evgeny], Moeller, M.[Michael], Cremers, D.[Daniel],
Low Rank Priors for Color Image Regularization,
EMMCVPR15(126-140).
Springer DOI 1504
BibRef

Lellmann, J.[Jan], Strekalovskiy, E.[Evgeny], Koetter, S.[Sabrina], Cremers, D.[Daniel],
Total Variation Regularization for Functions with Values in a Manifold,
ICCV13(2944-2951)
IEEE DOI 1403
angular data BibRef

Burger, M.[Martin], Papafitsoros, K.[Konstantinos], Papoutsellis, E.[Evangelos], Schönlieb, C.B.[Carola-Bibiane],
Infimal Convolution Regularisation Functionals of BV and L_p Spaces,
JMIV(55), No. 3, July 2016, pp. 343-369.
Springer DOI 1604
BibRef

Ke, R.[Rihuan], Schönlieb, C.B.[Carola-Bibiane],
Unsupervised Image Restoration Using Partially Linear Denoisers,
PAMI(44), No. 9, September 2022, pp. 5796-5812.
IEEE DOI 2208
Noise measurement, Noise reduction, TV, Image denoising, Training, Image restoration, Neural networks, Image denoising, deep learning, partially linear denoiser BibRef

Ke, R.[Rihuan],
Deep Variation Prior: Joint Image Denoising and Noise Variance Estimation Without Clean Data,
IP(33), 2024, pp. 2908-2923.
IEEE DOI 2405
Noise, Noise reduction, Noise measurement, Estimation, Training, Noise level, Image denoising, Image denoising, deep learning without ground truth BibRef

Calatroni, L.[Luca], de los Reyes, J.C.[Juan Carlos], Schönlieb, C.B.[Carola-Bibiane],
Infimal Convolution of Data Discrepancies for Mixed Noise Removal,
SIIMS(10), No. 3, 2017, pp. 1196-1233.
DOI Link 1710
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Zhang, B.[Benxin], Zhu, Z.B.[Zhi-Bin], Wang, S.[Shuo],
A simple primal-dual method for total variation image restoration,
JVCIR(38), No. 1, 2016, pp. 814-823.
Elsevier DOI 1605
Primal-dual method BibRef

Tao, S.Y.[Shu-Yin], Dong, W.[Wende], Xu, Z.H.[Zhi-Hai], Tang, Z.M.[Zhen-Min],
Fast total variation deconvolution for blurred image contaminated by Poisson noise,
JVCIR(38), No. 1, 2016, pp. 582-594.
Elsevier DOI 1605
Image restoration BibRef

Dong, W.[Wende], Tao, S.Y.[Shu-Yin], Xu, G.L.[Gui-Li], Chen, Y.T.[Yue-Ting],
Blind Deconvolution for Poissonian Blurred Image With Total Variation and L0-Norm Gradient Regularizations,
IP(30), 2021, pp. 1030-1043.
IEEE DOI 2012
Deconvolution, TV, Image restoration, Minimization, Mathematical model, Iterative methods, Estimation, total variation regularization BibRef

Chen, G.[Gao], Zhang, J.S.[Jia-Shu], Li, D.F.[De-Fang],
Fractional-order total variation combined with sparsifying transforms for compressive sensing sparse image reconstruction,
JVCIR(38), No. 1, 2016, pp. 407-422.
Elsevier DOI 1605
Two-dimensional compressive sensing BibRef

Chen, G.[Gao], Li, G.[Gang], Zhang, J.S.[Jia-Shu],
Tensor compressed video sensing reconstruction by combination of fractional-order total variation and sparsifying transform,
SP:IC(55), No. 1, 2017, pp. 146-156.
Elsevier DOI 1705
Compressed, video, sensing BibRef

Nelson, J.D.B., Nafornita, C., Isar, A.,
Semi-Local Scaling Exponent Estimation With Box-Penalty Constraints and Total-Variation Regularization,
IP(25), No. 7, July 2016, pp. 3167-3181.
IEEE DOI 1606
image processing BibRef

Kolmogorov, V.[Vladimir], Pock, T.[Thomas], Rolinek, M.[Michal],
Total Variation on a Tree,
SIIMS(9), No. 2, 2016, pp. 605-636.
DOI Link 1608
BibRef

Tang, L.M.[Li-Ming], Fang, Z.[Zhuang], Xiang, C.C.[Chang-Cheng], Chen, S.Q.[Shi-Qiang],
Image selective restoration using multi-scale variational decomposition,
JVCIR(40, Part B), No. 1, 2016, pp. 638-655.
Elsevier DOI 1610
Total variation BibRef

Thanh, D.N.H.[Dang Ngoc Hoang],
A variational approach to denoising problem,
ELCVIA(15), No. 2, 2016, pp. 19-21.
DOI Link 1611
BibRef

Langer, A.[Andreas],
Automated Parameter Selection for Total Variation Minimization in Image Restoration,
JMIV(57), No. 2, February 2017, pp. 239-268.
WWW Link. 1702
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Wijewardhana, U.L., Codreanu, M., Latva-aho, M.,
An Interior-Point Method for Modified Total Variation Exploiting Transform-Domain Sparsity,
SPLetters(24), No. 1, January 2017, pp. 56-60.
IEEE DOI 1702
Newton method BibRef

Deledalle, C.A.[Charles-Alban], Papadakis, N.[Nicolas], Salmon, J.[Joseph], Vaiter, S.[Samuel],
CLEAR: Covariant LEAst-Square Refitting with Applications to Image Restoration,
SIIMS(10), No. 1, 2017, pp. 243-284.
DOI Link 1704
BibRef
Earlier: A1, A2, A3, Only:
On Debiasing Restoration Algorithms: Applications to Total-Variation and Nonlocal-Means,
SSVM15(129-141).
Springer DOI 1506
BibRef

Berger, J.[Johannes], Lenzen, F.[Frank], Becker, F.[Florian], Neufeld, A.[Andreas], Schnörr, C.[Christoph],
Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE3 Based on Nonlinear Observations,
JMIV(58), No. 1, May 2017, pp. 102-129.
Springer DOI 1704
BibRef
Earlier: A1, A4, A3, A2, A5:
Second Order Minimum Energy Filtering on SE3 with Nonlinear Measurement Equations,
SSVM15(397-409).
Springer DOI 1506
BibRef

Lenzen, F.[Frank], Becker, F.[Florian], Lellmann, J.[Jan],
Adaptive Second-Order Total Variation: An Approach Aware of Slope Discontinuities,
SSVM13(61-73).
Springer DOI 1305
BibRef

Shen, Y., Liu, Q., Lou, S., Hou, Y.L.,
Wavelet-Based Total Variation and Nonlocal Similarity Model for Image Denoising,
SPLetters(24), No. 6, June 2017, pp. 877-881.
IEEE DOI 1705
Image edge detection, Image restoration, Noise reduction, TV, Wavelet coefficients, Wavelet domain, Birothogonal wavelet, heavy noise, nonlocal similarity, split Bregman, total, variation, (TV) BibRef

Serra, J.G., Testa, M., Molina, R., Katsaggelos, A.K.,
Bayesian K-SVD Using Fast Variational Inference,
IP(26), No. 7, July 2017, pp. 3344-3359.
IEEE DOI 1706
Adaptation models, Approximation algorithms, Bayes methods, Data models, Dictionaries, Transforms, Uncertainty, Bayesian modeling, denoising, dictionary learning, inpainting, k-svd, sparse representation, variational inference BibRef

Wang, X.Y.[Xiao-Yang], Peng, Z.M.[Zhen-Ming], Kong, D.[Dehui], Zhang, P.[Ping], He, Y.M.[Yan-Min],
Infrared dim target detection based on total variation regularization and principal component pursuit,
IVC(63), No. 1, 2017, pp. 1-9.
Elsevier DOI 1706
Infrared, images BibRef

Yun, J.D.[Joo Dong], Yang, S.J.[Seung-Joon],
ADMM in Krylov Subspace and Its Application to Total Variation Restoration of Spatially Variant Blur,
SIIMS(10), No. 2, 2017, pp. 484-507.
DOI Link 1708
BibRef

Liu, J.[Jun], Zheng, X.J.[Xiao-Jun],
A Block Nonlocal TV Method for Image Restoration,
SIIMS(10), No. 2, 2017, pp. 920-941.
DOI Link 1708
BibRef

Abergel, R.[Rémy], Moisan, L.[Lionel],
The Shannon Total Variation,
JMIV(59), No. 2, October 2017, pp. 341-370.
WWW Link. 1709
BibRef

Abergel, R.[Rémy], Louchet, C.[Cécile], Moisan, L.[Lionel], Zeng, T.Y.[Tie-Yong],
Total Variation Restoration of Images Corrupted by Poisson Noise with Iterated Conditional Expectations,
SSVM15(178-190).
Springer DOI 1506
BibRef

Li, Z.[Zhi], Malgouyres, F.[François], Zeng, T.Y.[Tie-Yong],
Regularized Non-local Total Variation and Application in Image Restoration,
JMIV(59), No. 2, October 2017, pp. 296-317.
WWW Link. 1709
BibRef

Hintermüller, M.[Michael], Rautenberg, C.N.[Carlos N.],
Optimal Selection of the Regularization Function in a Weighted Total Variation Model. Part I: Modelling and Theory,
JMIV(59), No. 3, November 2017, pp. 498-514.
Springer DOI 1710
BibRef

Hintermüller, M.[Michael], Rautenberg, C.N.[Carlos N.], Wu, T.[Tao], Langer, A.[Andreas],
Optimal Selection of the Regularization Function in a Weighted Total Variation Model. Part II: Algorithm, Its Analysis and Numerical Tests,
JMIV(59), No. 3, November 2017, pp. 515-533.
Springer DOI 1710
BibRef

Lasica, M.[Michal], Moll, S.[Salvador], Mucha, P.B.[Piotr B.],
Total Variation Denoising in L^1 Anisotropy,
SIIMS(10), No. 4, 2017, pp. 1691-1723.
DOI Link 1801
BibRef

Landrieu, L.[Loic], Obozinski, G.[Guillaume],
Cut Pursuit: Fast Algorithms to Learn Piecewise Constant Functions on General Weighted Graphs,
SIIMS(10), No. 4, 2017, pp. 1724-1766.
DOI Link 1801
BibRef

Zhao, W., Lu, H., Wang, D.,
Multisensor Image Fusion and Enhancement in Spectral Total Variation Domain,
MultMed(20), No. 4, April 2018, pp. 866-879.
IEEE DOI 1804
Feature extraction, Fuses, Image edge detection, Image fusion, Robot sensing systems, TV, Transforms, Adaptive gain function, spectral total variation (TV) BibRef

Xu, Y., Wu, Z., Chanussot, J., Dalla Mura, M., Bertozzi, A.L., Wei, Z.,
Low-Rank Decomposition and Total Variation Regularization of Hyperspectral Video Sequences,
GeoRS(56), No. 3, March 2018, pp. 1680-1694.
IEEE DOI 1804
Chemicals, Feature extraction, Hyperspectral imaging, Sparse matrices, TV, Video sequences, Detection, total variation (LRSTV) BibRef

Esser, E.[Ernie], Guasch, L.[Lluis], van Leeuwen, T.[Tristan], Aravkin, A.Y.[Aleksandr Y.], Herrmann, F.J.[Felix J.],
Total Variation Regularization Strategies in Full-Waveform Inversion,
SIIMS(11), No. 1, 2018, pp. 376-406.
DOI Link 1804
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Liu, P.F.[Peng-Fei], Xiao, L.[Liang], Li, T.[Tao],
Normal curvature-induced variational model for image restoration,
IET-IPR(12), No. 5, May 2018, pp. 679-689.
DOI Link 1804
BibRef

Liu, Y., Pados, D.A., Kim, J., Zhang, C.,
Reconstruction of Compressed-Sensed Multiview Video With Disparity- and Motion-Compensated Total Variation Minimization,
CirSysVideo(28), No. 6, June 2018, pp. 1288-1302.
IEEE DOI 1806
Cameras, Correlation, Decoding, Image reconstruction, Minimization, Streaming media, TV, 360° video, compressed sensing (CS), total variation (TV) minimization BibRef

Herrmann, M., Herzog, R., Kröner, H., Schmidt, S., Vidal, J.,
Analysis and an Interior-Point Approach for TV Image Reconstruction Problems on Smooth Surfaces,
SIIMS(11), No. 2, 2018, pp. 889-922.
DOI Link 1807
BibRef

Daei, S., Haddadi, F., Amini, A.,
Sample Complexity of Total Variation Minimization,
SPLetters(25), No. 8, August 2018, pp. 1151-1155.
IEEE DOI 1808
Gaussian processes, gradient methods, minimisation, Gaussian linear measurements, sharp phase transition behavior, total variation (TV) minimization BibRef

Du, H.Q.[Hui-Qian], Liu, Y.L.[Yi-Lin],
Minmax-concave total variation denoising,
SIViP(12), No. 6, September 2018, pp. 1027-1034.
WWW Link. 1808
BibRef

Yan, S.[Shi], Yu, Z.[Zihao], Liu, J.[Jun],
Non-parametric mixture model with TV spatial regularisation and its dual expectation maximisation algorithm,
IET-IPR(12), No. 9, September 2018, pp. 1655-1662.
DOI Link 1809
BibRef

Song, M.Z.[Ming-Zhu], Qu, H.S.[Hong-Song], Zhang, G.X.[Gui-Xiang], Tao, S.P.[Shu-Ping], Jin, G.[Guang],
A Variational Model for Sea Image Enhancement,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
BibRef

Hait-Fraenkel, E.[Ester], Gilboa, G.[Guy],
Spectral Total-Variation Local Scale Signatures for Image Manipulation and Fusion,
IP(28), No. 2, February 2019, pp. 880-895.
IEEE DOI 1811
TV, Eigenvalues and eigenfunctions, Transforms, Image edge detection, Sensitivity, Biomedical imaging, medical imagery BibRef

Hait-Fraenkel, E.[Ester], Gilboa, G.[Guy],
Revealing stable and unstable modes of denoisers through nonlinear eigenvalue analysis,
JVCIR(75), 2021, pp. 103041.
Elsevier DOI 2103
Eigenfunctions, Nonlinear operators, Denoising, Power iteration, Total-variation, EPLL BibRef

Zhang, G.Y.[Guo-Yun], Wang, J.P.[Jin-Ping], Zhang, X.F.[Xiao-Fei], Fei, H.Y.[Hong-Yan], Tu, B.[Bing],
Adaptive total variation-based spectral-spatial feature extraction of hyperspectral image,
JVCIR(56), 2018, pp. 150-159.
Elsevier DOI 1811
Hyperspectral image classification, Principal component analysis, Extreme learning machine BibRef

Sun, L.[Le], Zhan, T.M.[Tian-Ming], Wu, Z.B.[Ze-Bin], Xiao, L.[Liang], Jeon, B.W.[Byeung-Woo],
Hyperspectral Mixed Denoising via Spectral Difference-Induced Total Variation and Low-Rank Approximation,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef
Earlier: A1, A5, A3, A4, Only:
Hyperspectral Denoising Via Cross Total Variation-Regularized Unidirectional Nonlocal Low-Rank Tensor Approximation,
ICIP18(2900-2904)
IEEE DOI 1809
Tensile stress, Noise reduction, Databases, Hyperspectral imaging, Correlation, Optimization, Sparse matrices, non-local self-similarity BibRef

Liu, H.Y.[Hong-Yi], Li, H.Y.[Han-Yang], Wu, Z.B.[Ze-Bin], Wei, Z.H.[Zhi-Hui],
Hyperspectral Image Recovery Using Non-Convex Low-Rank Tensor Approximation,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
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Ben Said, A.[Ahmed], Hadjidj, R.[Rachid], Foufou, S.[Sebti],
Total Variation for Image Denoising Based on a Novel Smart Edge Detector: An Application to Medical Images,
JMIV(61), No. 1, January 2019, pp. 106-121.
Springer DOI 1901
BibRef

Bini, A.A.,
Image restoration via DOST and total variation regularisation,
IET-IPR(13), No. 3, February 2019, pp. 458-468.
DOI Link 1903
BibRef

He, C.[Chuan], Hu, C.H.[Chang-Hua], Qi, N.X.[Nai-Xin], Zhu, X.F.[Xiao-Fei], Liu, L.X.[Lian-Xiong],
Fast proximal splitting algorithm for constrained TGV-regularised image restoration and reconstruction,
IET-IPR(13), No. 4, March 2019, pp. 576-582.
DOI Link 1903
BibRef

Pang, Z.F.[Zhi-Feng], Zhou, Y.M.[Ya-Mei], Wu, T.T.[Ting-Ting], Li, D.J.[Ding-Jie],
Image denoising via a new anisotropic total-variation-based model,
SP:IC(74), 2019, pp. 140-152.
Elsevier DOI 1904
Image denoising, Anisotropic total variation, Alternating direction method of multipliers(ADMM), Weighted matrix BibRef

Herrmann, M.[Marc], Herzog, R.[Roland], Schmidt, S.[Stephan], Vidal-Núñez, J.[José], Wachsmuth, G.[Gerd],
Discrete Total Variation with Finite Elements and Applications to Imaging,
JMIV(61), No. 4, May 2019, pp. 411-431.
Springer DOI 1904
BibRef

Kumar, A., Ahmad, M.O.[M. Omair], Swamy, M.N.S.,
Tchebichef and Adaptive Steerable-Based Total Variation Model for Image Denoising,
IP(28), No. 6, June 2019, pp. 2921-2935.
IEEE DOI 1905
edge detection, image denoising, image restoration, iterative methods, minimisation, wavelet transforms, image denoising BibRef

Xue, F.[Feng], Liu, J.[Jiaqi], Ai, X.[Xia],
Recursive SURE for image recovery via total variation minimization,
SIViP(13), No. 4, June 2019, pp. 795-803.
Springer DOI 1906
BibRef

Jung, A., Tran, N.,
Localized Linear Regression in Networked Data,
SPLetters(26), No. 7, July 2019, pp. 1090-1094.
IEEE DOI 1906
Linear regression, Data models, Training, Convex functions, Estimation error, TV, Image processing, Compressed sensing, statistical learning BibRef

Merveille, O., Naegel, B., Talbot, H., Passat, N.,
nD Variational Restoration of Curvilinear Structures With Prior-Based Directional Regularization,
IP(28), No. 8, August 2019, pp. 3848-3859.
IEEE DOI 1907
gradient methods, image denoising, image resolution, image restoration, image segmentation, segmentation BibRef

Jia, Z., Ng, M.K.[Michael K.], Wang, W.[Wei],
Color Image Restoration by Saturation-Value Total Variation,
SIIMS(12), No. 2, 2019, pp. 972-1000.
DOI Link 1907
BibRef

Wang, W.[Wei], Yang, Y.M.[Yu-Ming], Ng, M.K.[Michael K.],
A Spatial Color Compensation Model Using Saturation-Value Total Variation,
SIIMS(15), No. 3, 2022, pp. 1400-1430.
DOI Link 2208
BibRef

Huang, Y.M.[Yu-Mei], Ng, M.K.[Michael K.], Wen, Y.W.[You-Wei],
A New Total Variation Method For Multiplicative Noise Removal,
SIIMS(2), No. 1, 2009, pp. 20-40. image denoising; multiplicative noise; total variation; convex function
DOI Link BibRef 0900

Wen, Y.W.[You-Wei], Ng, M.K.[Michael K.], Huang, Y.M.[Yu-Mei],
Efficient Total Variation Minimization Methods for Color Image Restoration,
IP(17), No. 11, November 2008, pp. 1-1.
IEEE DOI 0810
BibRef

Wang, W.[Wei], Ng, M.K.[Michael K.],
Color Image Restoration by Saturation-Value Total Variation Regularization on Vector Bundles,
SIIMS(14), No. 1, 2021, pp. 178-197.
DOI Link 2104
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Huang, C.Y.[Chao-Yan], Li, Z.[Zhi], Liu, Y.B.[Yu-Bing], Wu, T.T.[Ting-Ting], Zeng, T.Y.[Tie-Yong],
Quaternion-based weighted nuclear norm minimization for color image restoration,
PR(128), 2022, pp. 108665.
Elsevier DOI 2205
Quaternion representation, Color image restoration, Weighted nuclear norm, Variational method, Low-rank matrix analysis BibRef

Guo, Y.[Yu], Chen, G.Q.[Guo-Qing], Zeng, T.Y.[Tie-Yong], Jin, Q.Y.[Qi-Yu], Ng, M.K.P.[Michael Kwok-Po],
Quaternion Nuclear Norm Minus Frobenius Norm Minimization for color image reconstruction,
PR(158), 2025, pp. 110986.
Elsevier DOI 2411
Quaternion, Low rank, Color image, Low-level vision, Nuclear norm, Frobenius norm BibRef

Huang, C.Y.[Chao-Yan], Ng, M.K.[Michael K.], Wu, T.T.[Ting-Ting], Zeng, T.Y.[Tie-Yong],
Quaternion-Based Dictionary Learning and Saturation-Value Total Variation Regularization for Color Image Restoration,
MultMed(24), 2022, pp. 3769-3781.
IEEE DOI 2208
Visualization, Dictionaries, Image color analysis, Quaternions, Imaging, Color, Machine learning, Dictionary learning, total variation BibRef

Liao, H.Y.[Hai-Yong], Li, F.[Fang], Ng, M.K.[Michael K.],
Selection of regularization parameter in total variation image restoration,
JOSA-A(26), No. 11, November 2009, pp. 2311-2320.
DOI Link 0911
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Wang, W., Li, F., Ng, M.K.,
Structural Similarity-Based Nonlocal Variational Models for Image Restoration,
IP(28), No. 9, Sep. 2019, pp. 4260-4272.
IEEE DOI 1908
feature extraction, image restoration, image texture, iterative methods, structural similarity, structural similarity index BibRef

Ouyang, Y.[Yi],
Total variation constraint GAN for dynamic scene deblurring,
IVC(88), 2019, pp. 113-119.
Elsevier DOI 1908
Total variation, Deblur, GAN, Convolution neural network BibRef

Hosseini, A.[Alireza],
New discretization of total variation functional for image processing tasks,
SP:IC(78), 2019, pp. 62-76.
Elsevier DOI 1909
Mathematical image processing, Total variation, Vector field, Denoising, Resolution enhancement, Fenchel-dual theorem BibRef

Fan, L.W.[Lin-Wei], Li, X.M.[Xue-Mei], Fan, H.[Hui], Feng, Y.L.[Yan-Li], Zhang, C.M.[Cai-Ming],
Adaptive Texture-Preserving Denoising Method Using Gradient Histogram and Nonlocal Self-Similarity Priors,
CirSysVideo(29), No. 11, November 2019, pp. 3222-3235.
IEEE DOI 1911
Noise reduction, Histograms, Image denoising, Adaptation models, Laplace equations, TV, Fans, alternating minimization BibRef

Zhang, S., Huang, H., Fu, Y.,
Fast Parallel Implementation of Dual-Camera Compressive Hyperspectral Imaging System,
CirSysVideo(29), No. 11, November 2019, pp. 3404-3414.
IEEE DOI 1911
Image reconstruction, Hyperspectral imaging, Apertures, Reconstruction algorithms, TV, Compressive sensing (CS), GPU BibRef

Overgaard, N.C.[Niels Chr],
On the Taut String Interpretation and Other Properties of the Rudin-Osher-Fatemi Model in One Dimension,
JMIV(61), No. 9, November 2019, pp. 1276-1300.
Springer DOI 1911

See also Nonlinear total variation based noise removal algorithms. BibRef

Kirisits, C.[Clemens], Scherzer, O.[Otmar], Setterqvist, E.[Eric],
Invariant phi-Minimal Sets and Total Variation Denoising on Graphs,
SIIMS(12), No. 4, 2019, pp. 1643-1668.
DOI Link 1912
BibRef

Lee, C.O.[Chang-Ock], Park, J.H.[Jong-Ho],
Fast Nonoverlapping Block Jacobi Method for the Dual Rudin-Osher-Fatemi Model,
SIIMS(12), No. 4, 2019, pp. 2009-2034.
DOI Link 1912

See also Nonlinear total variation based noise removal algorithms. BibRef

Aghamiry, H.S.[Hossein S.], Gholami, A.[Ali], Operto, S.[Stéphane],
Compound Regularization of Full-Waveform Inversion for Imaging Piecewise Media,
GeoRS(58), No. 2, February 2020, pp. 1192-1204.
IEEE DOI 2001
TV, Compounds, Imaging, Mathematical model, Integrated circuits, Image reconstruction, Media, Compound regularization, wavefield reconstruction inversion (WRI) BibRef

Aghamiry, H.S.[Hossein S.], Gholami, A.[Ali], Operto, S.[Stéphane],
Complex-Valued Imaging with Total Variation Regularization: An Application to Full-Waveform Inversion in Visco-acoustic Media,
SIIMS(14), No. 1, 2021, pp. 58-91.
DOI Link 2104
BibRef

Hu, Y., Li, X., Gu, Y., Jacob, M.,
Hyperspectral Image Recovery Using Nonconvex Sparsity and Low-Rank Regularizations,
GeoRS(58), No. 1, January 2020, pp. 532-545.
IEEE DOI 2001
TV, Image restoration, Hyperspectral imaging, Correlation, Noise reduction, Optimization, Hyperspectral image (HSI), restoration BibRef

Yang, Y.H.[Yan-Hong], Chen, S.Y.[Sheng-Yong], Zheng, J.W.[Jian-Wei],
Moreau-Enhanced Total Variation and Subspace Factorization for Hyperspectral Denoising,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Yang, Y.H.[Yan-Hong], Zheng, J.W.[Jian-Wei], Chen, S.Y.[Sheng-Yong],
Local low-rank matrix recovery for hyperspectral image denoising with L0 gradient constraint,
PRL(135), 2020, pp. 167-172.
Elsevier DOI 2006
Hyperspectral image (HSI), Low-rank matrix recovery, L gradient, Nonconvex optimization, Denoising BibRef

Ye, J.[Jun], Zhang, X.[Xian],
Hyperspectral image restoration by subspace representation with low-rank constraint and spatial-spectral total variation,
IET-IPR(14), No. 2, February 2020, pp. 220-230.
DOI Link 2001
BibRef

Schultze, B., Censor, Y., Karbasi, P., Schubert, K.E., Schulte, R.W.,
An Improved Method of Total Variation Superiorization Applied to Reconstruction in Proton Computed Tomography,
MedImg(39), No. 2, February 2020, pp. 294-307.
IEEE DOI 2002
TV, Perturbation methods, Image reconstruction, Protons, Computed tomography, Image quality, Standards, total variation superiorization (TVS) BibRef

Effland, A.[Alexander], Kobler, E.[Erich], Kunisch, K.[Karl], Pock, T.[Thomas],
Variational Networks: An Optimal Control Approach to Early Stopping Variational Methods for Image Restoration,
JMIV(62), No. 3, April 2020, pp. 396-416.
Springer DOI 2004
BibRef

Kobler, E.[Erich], Klatzer, T.[Teresa], Hammernik, K.[Kerstin], Pock, T.[Thomas],
Variational Networks: Connecting Variational Methods and Deep Learning,
GCPR17(281-293).
Springer DOI 1711
Award, GCPR. BibRef

Kobler, E.[Erich], Effland, A.[Alexander], Kunisch, K.[Karl], Pock, T.[Thomas],
Total Deep Variation for Linear Inverse Problems,
CVPR20(7546-7555)
IEEE DOI 2008
Optimal control, Convolution, Training, Inverse problems, Task analysis, Kernel, Imaging BibRef

Chambolle, A., Holler, M., Pock, T.,
A Convex Variational Model for Learning Convolutional Image Atoms from Incomplete Data,
JMIV(62), No. 3, April 2020, pp. 417-444.
Springer DOI 2004
BibRef

Lima, J.A.[Jonathan A.], da Silva, F.B.[Felipe B.], von Borries, R.[Ricardo], Miosso, C.J.[Cristiano J.], Farias, M.C.Q.[Mylène C.Q.],
Isotropic and anisotropic filtering norm-minimization: A generalization of the TV and TGV minimizations using NESTA,
SP:IC(85), 2020, pp. 115856.
Elsevier DOI 2005
Compressive sensing, Filtering, NESTA, MRI BibRef

Tran, T.T.T., Pham, C.T., Kopylov, A.V., Nguyen, V.N.,
An Adaptive Variational Model for Medical Images Restoration,
PTVSBB19(219-224).
DOI Link 1912
BibRef

Demircan-Tureyen, E.[Ezgi], Kamasak, M.E.[Mustafa E.],
On the Direction Guidance in Structure Tensor Total Variation Based Denoising,
IbPRIA(I:89-100).
Springer DOI 1910
BibRef

Kirisits, C.[Clemens], Scherzer, O.[Otmar], Setterqvist, E.[Eric],
Preservation of Piecewise Constancy under TV Regularization with Rectilinear Anisotropy,
SSVM19(510-521).
Springer DOI 1909
BibRef

Burger, M.[Martin], Korolev, Y.[Yury], Schönlieb, C.B.[Carola-Bibiane], Stollenwerk, C.[Christiane],
A Total Variation Based Regularizer Promoting Piecewise-Lipschitz Reconstructions,
SSVM19(485-497).
Springer DOI 1909
BibRef

Parisotto, S.[Simone], Schönlieb, C.B.[Carola-Bibiane],
Total Directional Variation for Video Denoising,
SSVM19(522-534).
Springer DOI 1909
BibRef

Rasch, J., Warno, V., Ptatt, J., Tischendorf, C., Marpe, D., Schwarz, H., Wiegand, T.,
A Signal Adaptive Diffusion Filter for Video Coding Using Directional Total Variation,
ICIP18(2570-2574)
IEEE DOI 1809
Encoding, UHDTV, Video coding, Video codecs, Minimization, Image edge detection, Smoothing methods, Alternating Direction Method of Multipliers (ADMM) BibRef

Smith, R., Basarab, A., Georgeot, B., Kouamé, D.,
Adaptive transform via quantum signal processing: Application to signal and image denoising,
ICIP18(1523-1527)
IEEE DOI 1809
Noise reduction, Transforms, Quantum mechanics, Noise measurement, Wave functions, TV, Signal to noise ratio, quantum mechanics BibRef

Liu, H., Xiong, R., Zhang, X., Zhang, Y., Ma, S., Gao, W.,
Nonlocal Gradient Sparsity Regularization for Image Restoration,
CirSysVideo(27), No. 9, September 2017, pp. 1909-1921.
IEEE DOI 1709
Adaptation models, Estimation, Image edge detection, Image restoration, Predictive models, TV, Transforms, Content-adaptive modeling, gradient sparsity, image restoration, nonlocal (NL) similarity, total, variation, (TV), regularization BibRef

Liu, H., Zhang, X., Xiong, R.,
Content-adaptive low rank regularization for image denoising,
ICIP16(3091-3095)
IEEE DOI 1610
Bayes methods BibRef

Peng, J.J.[Jiang-Jun], Xie, Q.[Qi], Zhao, Q.[Qian], Wang, Y.[Yao], Yee, L.[Leung], Meng, D.Y.[De-Yu],
Enhanced 3DTV Regularization and Its Applications on HSI Denoising and Compressed Sensing,
IP(29), 2020, pp. 7889-7903.
IEEE DOI 2007
Noise reduction, Task analysis, Compressed sensing, Correlation, Tensile stress, TV, Sensors, Hyperspectral image, denoising, total variation BibRef

Chambolle, A.[Antonin], Pock, T.[Thomas],
Crouzeix-Raviart Approximation of the Total Variation on Simplicial Meshes,
JMIV(62), No. 6-7, July 2020, pp. 872-899.
Springer DOI 2007
BibRef

Lin, X.L.[Xue-Lei], Ng, M.K.[Michael K.], Zhao, X.L.[Xi-Le],
Tensor Factorization with Total Variation and Tikhonov Regularization for Low-Rank Tensor Completion in Imaging Data,
JMIV(62), No. 6-7, July 2020, pp. 900-918.
Springer DOI 2007
BibRef

Thanh, D.N.H.[Dang N. H.], Prasath, V.B.S.[V. B. Surya], Hieu, L.M.[Le Minh], Dvoenko, S.[Sergey],
An adaptive method for image restoration based on high-order total variation and inverse gradient,
SIViP(14), No. 6, September 2020, pp. 1189-1197.
Springer DOI 2008
BibRef

Rahiman, V.A.[V. Abdu], George, S.N.[Sudhish N.],
Multi-frame image super resolution using spatially weighted total variation regularisations,
IET-IPR(14), No. 10, August 2020, pp. 2187-2194.
DOI Link 2008
BibRef

Azimpour, P., Bahraini, T., Yazdi, H.S.,
Hyperspectral Image Denoising via Clustering-Based Latent Variable in Variational Bayesian Framework,
GeoRS(59), No. 4, April 2021, pp. 3266-3276.
IEEE DOI 2104
Runtime, Gaussian noise, Simulation, Noise reduction, Switches, Bayes methods, Mathematical model, nonindependent and identically distributed (non-i.i.d.) noise modeling BibRef

Zeng, H., Xie, X., Cui, H., Yin, H., Ning, J.,
Hyperspectral Image Restoration via Global L1-2 Spatial-Spectral Total Variation Regularized Local Low-Rank Tensor Recovery,
GeoRS(59), No. 4, April 2021, pp. 3309-3325.
IEEE DOI 2104
Tensile stress, Image restoration, Noise reduction, TV, Sparse matrices, Hyperspectral sensors, Solid modeling, restoration BibRef

Wang, Y.M.[Ying-Mei], Wang, Z.D.[Zhen-Dong],
Image denoising method based on variable exponential fractional-integer-order total variation and tight frame sparse regularization,
IET-IPR(15), No. 1, 2021, pp. 101-114.
DOI Link 2106
BibRef

Wang, M.H.[Ming-Hua], Wang, Q.[Qiang], Chanussot, J.[Jocelyn], Hong, D.F.[Dan-Feng],
L0-L1 Hybrid Total Variation Regularization and its Applications on Hyperspectral Image Mixed Noise Removal and Compressed Sensing,
GeoRS(59), No. 9, September 2021, pp. 7695-7710.
IEEE DOI 2109
TV, Tensors, Image restoration, Image edge detection, Compressed sensing, Minimization, Noise reduction, hyperspectral image (HSI) denoising BibRef

Li, M.M.[Meng-Meng], Li, B.Z.[Bing-Zhao],
A novel weighted total variation model for image denoising,
IET-IPR(15), No. 12, 2021, pp. 2749-2760.
DOI Link 2109
BibRef
And: Corrigendum: IET-IPR(16), No. 1, 2022, pp. 285-288.
DOI Link 2112
BibRef

Demircan-Tureyen, E.[Ezgi], Kamasak, M.E.[Mustafa E.],
Adaptive direction-guided structure tensor total variation,
SP:IC(99), 2021, pp. 116497.
Elsevier DOI 2111
Variational models, Image denoising, Directional total variation, Structure tensor, Inverse problems BibRef

Li, C.C.[Chun-Chao], Tang, X.[Xuebin], Shi, L.[Lulu], Peng, Y.X.[Yuan-Xi], Tang, Y.H.[Yu-Hua],
A Two-Staged Feature Extraction Method Based on Total Variation for Hyperspectral Images,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Soh, J.W.[Jae Woong], Cho, N.I.[Nam Ik],
Variational Deep Image Restoration,
IP(31), 2022, pp. 4363-4376.
IEEE DOI 2207
Image restoration, Degradation, Task analysis, Training, Noise reduction, Superresolution, Image coding, Image restoration, JPEG compression artifacts reduction BibRef

Rahiche, A.[Abderrahmane], Cheriet, M.[Mohamed],
Nonlinear Orthogonal NMF on the Stiefel Manifold With Graph-Based Total Variation Regularization,
SPLetters(29), 2022, pp. 1457-1461.
IEEE DOI 2207
Kernel, Data models, Optimization, TV, Standards, Matrix converters, Linear programming, multispectral image BibRef

Huo, L.[Limei], Chen, W.[Wengu], Ge, H.[Huanmin], Ng, M.K.[Michael K.],
Stable Image Reconstruction Using Transformed Total Variation Minimization,
SIIMS(15), No. 3, 2022, pp. 1104-1139.
DOI Link 2208
BibRef

de los Reyes, J.C.[Juan Carlos], Villacis, D.[David],
Optimality Conditions for Bilevel Imaging Learning Problems with Total Variation Regularization,
SIIMS(15), No. 4, 2022, pp. 1646-1689.
DOI Link 2211
BibRef

Pagliari, V.[Valerio], Papafitsoros, K.[Kostas], Raibta, B.[Bogdan], Vikelis, A.[Andreas],
Bilevel Training Schemes in Imaging for Total Variation: Type Functionals with Convex Integrands,
SIIMS(15), No. 4, 2022, pp. 1690-1728.
DOI Link 2211
BibRef

Qi, H.Q.[Hui-Qing], Tan, S.L.[Sheng-Li], Li, Z.C.[Zhi-Chao],
Anisotropic Weighted Total Variation Feature Fusion Network for Remote Sensing Image Denoising,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
BibRef

El Bouchairi, I.[Imad], Elmoataz, A.[Abderrahim], Fadili, J.[Jalal],
Nonlocal Perimeters and Curvature Flows on Graphs with Applications in Image Processing and High-Dimensional Data Classification,
SIIMS(16), No. 1, 2023, pp. 368-392.
DOI Link 2303
BibRef

Kumar, K.P.[K. Praveen], Narasimhulu, C.V.[C. Venkata], Prasad, K.S.[K. Satya],
2D Wavelet Tree Ordering Based Localized Total Variation Model for Efficient Image Restoration,
IJIG(23), No. 3 2023, pp. 2240009.
DOI Link 2306
BibRef

Li, J.[Jun], Han, Y.X.[Yu-Xuan], Gao, Y.[Yin], Li, Q.M.[Qi-Ming], Wang, S.[Sumei],
An Enhance Relative Total Variation With BF Model for Edge-Preserving Image Smoothing,
CirSysVideo(33), No. 10, October 2023, pp. 5420-5432.
IEEE DOI 2310
BibRef

Kuric, M.[Muhamed], Ahmetspahic, J.[Jan], Pock, T.[Thomas],
Total Generalized Variation on a Tree,
SIIMS(17), No. 2, 2024, pp. 1040-1077.
DOI Link 2407
BibRef

Liu, Y.[Yuan], Wu, C.L.[Chun-Lin], Zeng, C.[Chao],
Non-Lipschitz Variational Models and their Iteratively Reweighted Least Squares Algorithms for Image Denoising on Surfaces,
SIIMS(17), No. 2, 2024, pp. 1255-1283.
DOI Link 2407
BibRef

Yi, L.X.[Li-Xuan], Zhao, Q.[Qian], Xu, Z.B.[Zong-Ben],
Hyperspectral Image Denoising by Pixel-Wise Noise Modeling and TV-Oriented Deep Image Prior,
RS(16), No. 15, 2024, pp. 2694.
DOI Link 2408
BibRef

Pan, Z.S.[Zhong-Shan], Liu, Z.T.[Zhao-Ting], Luo, K.[Kang], Zhao, Y.[Yinan], Xu, X.R.[Xiao-Rong],
Compressive Sensing Total-Variation Primal-Dual Algorithms for Image Reconstruction,
SPLetters(31), 2024, pp. 1965-1969.
IEEE DOI 2408
Image reconstruction, Signal processing algorithms, TV, Compressed sensing, Vectors, Convex functions, Convergence, total-variation BibRef

Yue, Z.S.[Zong-Sheng], Yong, H.W.[Hong-Wei], Zhao, Q.[Qian], Zhang, L.[Lei], Meng, D.Y.[De-Yu], Wong, K.Y.K.[Kwan-Yee K.],
Deep Variational Network Toward Blind Image Restoration,
PAMI(46), No. 11, November 2024, pp. 7011-7026.
IEEE DOI 2410
Task analysis, Degradation, Image restoration, Testing, Superresolution, Noise reduction, Bayes methods, Image restoration, variational inference BibRef

Li, L.[Lan], Song, M.P.[Mei-Ping], Zhang, Q.[Qiang], Dong, Y.S.[Yu-Shuai], Wang, Y.[Yulei], Yuan, Q.Q.[Qiang-Qiang],
Local Extremum Constrained Total Variation Model for Natural and Hyperspectral Image Non-Blind Deblurring,
CirSysVideo(34), No. 9, September 2024, pp. 8547-8561.
IEEE DOI 2410
TV, Image restoration, Adaptation models, Kernel, Computational modeling, Noise, Image edge detection, total variation (TV) BibRef

Gong, Y.H.[Yuan-Hao], Yue, G.H.[Guang-Hui],
Start-TV: A Closed-Form Initialization for Total Variation Models,
ICIP24(1554-1559)
IEEE DOI 2411
Optical filters, Closed-form solutions, TV, Computational modeling, Superresolution, Estimation, Numerical models, start-TV, TV, initialization BibRef

Gong, Y.H.[Yuan-Hao],
A Multiscale Residual Solver for Total Variation Models,
ICIP23(151-155)
IEEE DOI 2312
BibRef

Gong, Y.H.[Yuan-Hao],
Imposing Total Variation Prior Into Guided Filter,
ICIP23(156-160)
IEEE DOI 2312
BibRef

Bogensperger, L.[Lea], Chambolle, A.[Antonin], Effland, A.[Alexander], Pock, T.[Thomas],
Learned Discretization Schemes for the Second-order Total Generalized Variation,
SSVM23(484-497).
Springer DOI 2307
BibRef

Yoshida, R.[Rino], Kodama, K.[Kazuya], Vu, H.[Huy], Cheung, G.[Gene], Hamamoto, T.[Takayuki],
Unrolling Graph Total Variation for Light Field Image Denoising,
ICIP22(1262-2166)
IEEE DOI 2211
Visualization, Image edge detection, Low-pass filters, Light fields, Robustness, Numerical models, convolutional neural nets BibRef

Qiu, H.Q.[Hai-Quan], Wang, Y.[Yao], Meng, D.Y.[De-Yu],
Effective Snapshot Compressive-spectral Imaging via Deep Denoising and Total Variation Priors,
CVPR21(9123-9132)
IEEE DOI 2111
Image coding, TV, Noise reduction, Imaging, Sensors, Optimization BibRef

Prost, J.[Jean], Houdard, A.[Antoine], Almansa, A.[Andrés], Papadakis, N.[Nicolas],
Learning Local Regularization for Variational Image Restoration,
SSVM21(358-370).
Springer DOI 2106
BibRef

de Castro, Y.[Yohann], Duval, V.[Vincent], Petit, R.[Romain],
Towards Off-the-grid Algorithms for Total Variation Regularized Inverse Problems,
SSVM21(553-564).
Springer DOI 2106
BibRef

Cohen, I.[Ido], Berkov, T.[Tom], Gilboa, G.[Guy],
Total-variation Mode Decomposition,
SSVM21(52-64).
Springer DOI 2106
BibRef

Ishihara, S., Kodama, K., Hamamoto, T.,
A Study On Light Field Denoising For 3d Consistent Visualization,
ICIP20(2800-2804)
IEEE DOI 2011
Noise reduction, Correlation, Image restoration, Smoothing methods, total variation BibRef

He, C., Hu, C., Li, X.,
A parallel linearized ADMM with application to multichannel tgv-based image restoration,
ICIP17(1187-1191)
IEEE DOI 1803
Acceleration, Convergence, Convex functions, Image restoration, Imaging, Inverse problems, TV, Nonsmooth optimization, PLADMM, TGV, image restoration BibRef

Yokota, T., Hontani, H.,
Simultaneous Visual Data Completion and Denoising Based on Tensor Rank and Total Variation Minimization and Its Primal-Dual Splitting Algorithm,
CVPR17(3843-3851)
IEEE DOI 1711
Convex functions, Minimization, Noise measurement, Noise reduction, Optimization, TV, Tensile, stress BibRef

Demircan-Tureyen, E.[Ezgi], Kamasak, M.E.[Mustafa E.],
Directional Total Variation Based Image Deconvolution with Unknown Boundaries,
CAIP17(II: 473-484).
Springer DOI 1708
BibRef

Wang, R.[Rui], Wang, G.Y.[Guo-Yu],
Medical X-ray image enhancement method based on TV-homomorphic filter,
ICIVC17(315-318)
IEEE DOI 1708
Histograms, Image enhancement, Information filtering, Medical diagnostic imaging, TV, X-ray imaging, X-ray image, image denoising, image enhancement, medical image, total, variation, (TV) BibRef

Said, A.B.[Ahmed Ben], Foufou, S.[Sebti],
Modified total variation regularization using fuzzy complement for image denoising,
ICVNZ15(1-6)
IEEE DOI 1701
edge detection BibRef

Yuan, X.,
Generalized alternating projection based total variation minimization for compressive sensing,
ICIP16(2539-2543)
IEEE DOI 1610
Apertures BibRef

Tierney, S., Guo, Y., Gao, J.,
Selective Multi-Source Total Variation Image Restoration,
DICTA15(1-8)
IEEE DOI 1603
image denoising BibRef