5.3.9.3 Total Variation Restoration, TV Restoration

Chapter Contents (Back)
Noise Removal. Total Variation. Variational Reconstruction. Denoising.

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

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.
WWW Link. 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

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.
WWW Link. 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

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

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

Wu, C.L.[Chun-Lin], Tai, X.C.[Xue-Cheng],
Augmented Lagrangian Method, Dual Methods, and Split Bregman Iteration For ROF, Vectorial TV, and High Order Models,
SIIMS(3), No. 3, 2010, pp. 300-339.
DOI Link BibRef 1000
Earlier: A2, A1:
Augmented Lagrangian Method, Dual Methods and Split Bregman Iteration for ROF Model,
SSVM09(502-513).
Springer DOI 0906
augmented Lagrangian method; dual method; split Bregman iteration; ROF model; total variation See also Orientation-Matching Minimization for Image Denoising and Inpainting. 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. -.
WWW 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

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

Jung, M.[Miyoun], Kang, M.J.[Myung-Joo],
Simultaneous Cartoon and Texture Image Restoration with Higher-Order Regularization,
SIIMS(8), No. 1, 2015, pp. 721-756.
DOI Link 1504
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

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., Plataniotis, K.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., Plataniotis, K.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, Image edge detection, Interpolation, Kernel, Mathematical model, Two dimensional displays, Canny edge detection, MaxFlat design, directional differentiation, high order derivatives, image interpolation, image sharpening, low, pass/full, band. 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],
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

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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.[Yifei], Zeng, T.[Tieyong], 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
BibRef

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

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
BibRef

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
BibRef

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

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
BibRef

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

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
BibRef

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.[Seungjoon],
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.[Tieyong],
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.
WWW Link. 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.
WWW Link. 1710
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

Rui, W.[Wang], Guoyu, W.[Wang],
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

Moeller, M.[Michael], Diebold, J., Gilboa, G.[Guy], Cremers, D.,
Learning Nonlinear Spectral Filters for Color Image Reconstruction,
ICCV15(289-297)
IEEE DOI 1602
Color See also Total Variation Spectral Framework for Scale and Texture Analysis, A. BibRef

Lu, Z.B.[Zhen-Bo], Li, H.Q.[Hou-Qiang], Li, W.P.[Wei-Ping],
A Bayesian adaptive weighted total generalized variation model for image restoration,
ICIP15(492-496)
IEEE DOI 1512
Adaptive Learning; Bayesian Theory; Total Generalized Variation BibRef

Hosseini, M.S.[Mahdi S.], Plataniotis, K.N.[Konstantinos N.],
Sparse tensor recovery via combined first and second order high-accuracy total variation,
ICIP15(701-705)
IEEE DOI 1512
alternating direction method of multipliers BibRef

Guo, X.J.[Xiao-Jie], Ma, Y.[Yi],
Generalized Tensor Total Variation minimization for visual data recovery?,
CVPR15(3603-3611)
IEEE DOI 1510
BibRef

Baust, M.[Maximilian], Demaret, L.[Laurent], Storath, M.[Martin], Navab, N.[Nassir], Weinmann, A.[Andreas],
Total variation regularization of shape signals,
CVPR15(2075-2083)
IEEE DOI 1510
BibRef

Brinkmann, E.M.[Eva-Maria], Burger, M.[Martin], Grah, J.[Joana],
Regularization with Sparse Vector Fields: From Image Compression to TV-type Reconstruction,
SSVM15(191-202).
Springer DOI 1506
BibRef

Lenzen, F.[Frank], Berger, J.[Johannes],
Solution-Driven Adaptive Total Variation Regularization,
SSVM15(203-215).
Springer DOI 1506
BibRef

Aujol, J.F.[Jean-François], Gilboa, G.[Guy], Papadakis, N.[Nicolas],
Fundamentals of Non-Local Total Variation Spectral Theory,
SSVM15(66-77).
Springer DOI 1506
BibRef

Rodreguez, P.[Paul], Wohlberg, B.[Brendt],
Performance comparison of iterative reweighting methods for total variation regularization,
ICIP14(1758-1762)
IEEE DOI 1502
Accuracy BibRef

Ozere, S.[Solene], Le Guyader, C.[Carole],
A joint segmentation-registration framework based on weighted total variation and nonlinear elasticity principles,
ICIP14(3552-3556)
IEEE DOI 1502
Biological system modeling BibRef

Li, T.[Ting], Papamichalis, P.E.[Panos E.],
A novel total variation optimization method and its application on blind super-resolution,
ICIP14(3892-3896)
IEEE DOI 1502
Equations BibRef

Bonettini, S.[Silvia], Benfenati, A.[Alessandro], Ruggiero, V.[Valeria],
Primal-dual first order methods for total variation image restoration in presence of poisson noise,
ICIP14(4156-4160)
IEEE DOI 1502
Convergence BibRef

Jalalzai, K.[Khalid], Chambolle, A.[Antonin],
Properties of minimizers of the total variation and of the solutions of the total variation flow,
ICIP14(4832-4836)
IEEE DOI 1502
Active contours BibRef

Ono, S.[Shunsuke], Yamada, I.[Isao],
Decorrelated Vectorial Total Variation,
CVPR14(4090-4097)
IEEE DOI 1409
BibRef

Chan, R.H.[Raymond H.], Liang, H.X.[Hai-Xia],
Half-Quadratic Algorithm for lp-lq Problems with Applications to TV-l1 Image Restoration and Compressive Sensing,
Optimization11(78-103).
Springer DOI 1405
BibRef

Oh, A.K.[Albert K.], Harmany, Z.T.[Zachary T.], Willett, R.M.[Rebecca M.],
Logarithmic total variation regularization for cross-validation in photon-limited imaging,
ICIP13(484-488)
IEEE DOI 1402
Image reconstruction BibRef

Loosli, C.[Cédric], Lecellier, F.[François],
A Color-Based Selective and Interactive Filter Using Weighted TV,
CAIP13(II:315-323).
Springer DOI 1311
BibRef

Martín, A.[Adrián], Schiavi, E.[Emanuele],
Automatic Total Generalized Variation-Based DTI Rician Denoising,
ICIAR13(581-588).
Springer DOI 1307
BibRef

Miyata, T.[Takamichi],
Inter-channel relation based vectorial total variation for color image recovery,
ICIP15(2251-2255)
IEEE DOI 1512
BibRef
Earlier:
L infinity total generalized variation for color image recovery,
ICIP13(449-453)
IEEE DOI 1402
Total variation. Color BibRef

Miyata, T.[Takamichi], Sakai, Y.[Yoshinori],
Vectorized total variation defined by weighted L infinity norm for utilizing inter channel dependency,
ICIP12(3057-3060).
IEEE DOI 1302
BibRef

Zhang, H.,
Hyperspectral Image Denoising With Cubic Total Variation Model,
AnnalsPRS(I-7), No. 2012, pp. 95-98.
HTML Version. 1209
BibRef

Xiao, L.[Liang], Huang, L.[Lili], Zhang, F.[Fanbiao],
Perceptual Saliency Driven Total Variation for Image Denoising Using Tensor Voting,
ICIG11(111-116).
IEEE DOI 1109
BibRef

Ono, S.[Shunsuke], Miyata, T.[Takamichi], Yamaoka, K.[Katsunori],
Total variation-wavelet-curvelet regularized optimization for image restoration,
ICIP11(2665-2668).
IEEE DOI 1201
BibRef

Ciril, I.[Igor], Darbon, J.[Jérôme],
Image Denoising with a Constrained Discrete Total Variation Scale Space,
DGCI11(465-476).
Springer DOI 1104
BibRef

Shu, X.B.[Xian-Biao], Ahuja, N.[Narendra],
Hybrid Compressive Sampling via a New Total Variation TVL1,
ECCV10(VI: 393-404).
Springer DOI 1009
I.e. insufficient by Nyquist/Shannon sampling theorem. BibRef

Shishkin, S.L.[Serge L.], Wang, H.C.[Hong-Cheng], Hagen, G.S.[Gregory S.],
Total Variation Minimization with Separable Sensing Operator,
ICISP10(86-93).
Springer DOI 1006
for compressed imaging. solve coupled Sylvester equations rather than iterative optimization procedure. Much faster. BibRef

Zeng, T.Y.[Tie-Yong],
Incorporating known features into a total variation dictionary model for source separation,
ICIP08(577-580).
IEEE DOI 0810
BibRef

Figueiredo, M.A.T., Dias, J.B., Oliveira, J.P., Nowak, R.D.[Robert D.],
On Total Variation Denoising: A New Majorization-Minimization Algorithm and an Experimental Comparison with Wavalet Denoising,
ICIP06(2633-2636).
IEEE DOI 0610
BibRef

Yu, G.Q.[Guo-Qiang], Li, L.[Liang], Gu, J.W.[Jian-Wei], Zhang, L.[Li],
Total Variation Based Iterative Image Reconstruction,
CVBIA05(526-534).
Springer DOI 0601
BibRef

Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Noise Removal, Adaptive, Non-linear Techniques .


Last update:Nov 18, 2017 at 20:56:18