4 Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar

4.1 Regularization Theory and Practice

Chapter Contents (Back)
Regularization. Regularization has been compared to a neural network with one hidden layer.

Tikhonov, A.N.,
The Regularization of Ill-Posed Problems,
Dokl. Akad. Nauk.(SSR 153), No. 1, 1963, pp. 49-52. BibRef 6300

Arsenin, V.Y.,
Regularization Method,
USSR Computational Math(8), 1968. BibRef 6800

Good, I.J., Gaskins, R.A.,
Nonparametric Roughness Penalties for Preobability Densities,
Biometrika(58), 1971, pp. 255-277. BibRef 7100

Shahraray, B., and Anderson, D.J.,
Optimal Estiamtion of Contour Properties by Cross-Validated Regularization,
PAMI(11), No. 6, June 1989, pp. 600-610.
IEEE DOI Analysis of parameters in regularization. BibRef 8906

Lee, D., and Pavlidis, T.,
One-Dimensional Regularization with Discontinuities,
PAMI(10), No. 6, November 1988, pp. 822-829.
IEEE DOI BibRef 8811
Earlier: ICCV87(572-577). BibRef

Terzopoulos, D.[Demetri],
Regularization of Inverse Visual Problems Involving Discontinuities,
PAMI(8), No. 4, July 1986, pp. 413-424. A proposal of stabilizing functions for use in inverse vision problems. There are a lot of references, and this may really go with his relaxation papers. BibRef 8607

Terzopoulos, D.[Demetri],
Visual Modelling,
BMVC91(xx-yy).
PDF File. 9109
BibRef

Terzopoulos, D.[Demetri],
Controlled-Smoothness Stabilizers fo the Regularization of Ill-Posed Visual Problems Involving Discontinuities,
DARPA84(225-229). BibRef 8400

Poggio, T., and Girosi, F.,
Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks,
Science(247), No. 4945, February 23, 1990. BibRef 9002

Girosi, F., Jones, M.J., Poggio, T.,
Regularization Theory and Neural Networks Architectures,
NeurComp(7), No. 2, March 1995, pp. 219-269. BibRef 9503

Poggio, T., and Girosi, F.,
Networks for Approximation and Learning,
PIEEE(78), No. 9, September 1990, pp. 1481-1497. BibRef 9009
Earlier:
A Theory of Networks for Approximation and Learning,
MIT AI-TR-1140, 1989. BibRef

Poggio, T.A., Torre, V., and Koch, C.,
Computational Vision and Regularization Theory,
Nature(317), 1985, pp. 314-319. BibRef 8500
Earlier: without A3:
Ill-Posed Problems and Regularization Analysis in Early Vision,
DARPA84(257-263). BibRef
And: MIT AI Memo-773, April 1984.
WWW Link. Computational Vision. A presentation of the basics of regularization and what it is intended to solve. BibRef

Taratorin, A.M., Sideman, S.,
Constrained regularized differentiation,
PAMI(16), No. 1, January 1994, pp. 88-92.
IEEE DOI 0401
BibRef

Marroquin, J.L.[Jose L.], Velasco, F.A.[Fernando A.], Rivera, M.[Mariano], and Nakamura, M.[Miguel],
Gauss-Markov Measure Field Models for Low-Level Vision,
PAMI(23), No. 4, April 2001, pp. 337-348.
IEEE DOI 0104
Model using Bayesian Estimation Theory with prior MRF models. Applied to segmentation, texture directions, classification, quantization. BibRef

Marroquin, J.L., Mitter, S.K., and Poggio, T.A.,
Probabilistic Solution of Ill-Posed Problems in Computational Vision,
ASAJ(82), No. 397, March 1987, pp. 76-89. BibRef 8703
Earlier: DARPA85(293-309). BibRef
And: MIT AI Memo-97, March 1987. BibRef

Marroquin, J.L.,
Deterministic Bayesian Estimation of Markovian Random Fields with Applications to Computational Vision,
ICCV87(597-601). BibRef 8700

Marroquin, J.L.[Jose Luis],
Probabilistic Solution of Inverse Problems,
MIT AI-TR-860, September 1985. BibRef 8509 Ph.D.Thesis. 1985.
WWW Link. BibRef

Bertero, M., Poggio, T.A., and Torre, V.,
Ill-Posed Problems in Early Vision,
PIEEE(76), No. 8, August 1988, pp. 869-889. BibRef 8808
Earlier: MIT AI Memo924, May 1987.
WWW Link. BibRef

Poggio, T.A.,
Early Vision: From Computational Structure to Algorithms and Parallel Hardware,
CVGIP(31), No. 2, August 1985, pp. 139-155.
Elsevier DOI See also Vision by Man and Machine. BibRef 8508

Verri, A.[Alessandro], Poggio, T.[Tomaso],
Regularization Theory and Shape Constraints,
MIT AI Memo-916, September 1986. BibRef 8609

Karayiannis, N.B., and Venetsanopoulos, A.N.,
Regularization Theory in Image Restoration: The Stabilizing Functional Approach,
ASSP(38), No. 7, July 1990, pp. 1155-1179. BibRef 9007

Unser, M., Aldroubi, A., and Eden, M.,
Recursive Regularization Filters: Design, Properties, and Applications,
PAMI(13), No. 3, March 1991, pp. 272-277.
IEEE DOI BibRef 9103

Thompson, A.M., Brown, J.C., Kay, J.W., and Titterington, D.M.,
A Study of Methods of Choosing the Smoothing Parameter in Image Restoration by Regularization,
PAMI(13), No. 4, April 1991, pp. 326-339.
IEEE DOI BibRef 9104

Archer, G., Titterington, D.M.,
On Some Bayesian/Regularization Methods for Image Restoration,
IP(4), No. 7, July 1995, pp. 989-995.
IEEE DOI 0402
Restoration. See also Bayesian Image Restoration: An Application to Edge-Preserving Surface Recovery. BibRef

Tanaka, K., Titterington, D.M.,
Probabilistic image processing based on the Q-ising model by means of the mean field method and loopy belief propagation,
ICPR04(II: 40-43).
IEEE DOI 0409
BibRef

Kang, M.G., Katsaggelos, A.K.,
General Choice of the Regularization Functional in Regularized Image-Restoration,
IP(4), No. 5, May 1995, pp. 594-602.
IEEE DOI See also Simultaneous Multichannel Image Restoration and Estimation of the Regularization Parameters. BibRef 9505

Kang, M.G., Katsaggelos, A.K.,
Simultaneous Multichannel Image Restoration and Estimation of the Regularization Parameters,
IP(6), No. 5, May 1997, pp. 774-778.
IEEE DOI 9705
See also General Choice of the Regularization Functional in Regularized Image-Restoration. BibRef

Hong, M.C., Kang, M.G., and Katsaggelos, A.K.,
An Iterative Weighted Regularized Algorithm for Improving the Resolution of Video Sequences,
ICIP97(II: 474-477).
IEEE DOI BibRef 9700

Shulman, D.,
Regularization of Inverse Problems in Low-Level Vision While Preserving Discontinuities,
Ph.D.Thesis (CS), Univ. of Maryland, August 1990. How to deal with edges in a regularization function. BibRef 9008

Stevenson, R.L., Schmitz, B.E., Delp, E.J.,
Discontinuity Preserving Regularization of Inverse Visual Problems,
SMC(24), No. 3, March 1994, pp. 455-469. BibRef 9403

Stevenson, R.L.[Robert L.], and Delp, E.J.[Edward J.],
Fitting Curves with Discontinuities,
Robust90(xx). BibRef 9000

Reeves, S.J., and Higdon, A.C.,
Perceptual Evaluation of the Mean Square Error Choice of Regularization Parameter,
IP(4), No. 1, January 1995, pp. 107-110.
IEEE DOI Human evaluation of the results. BibRef 9501

Li, S.Z.,
On Discontinuity-Adaptive Smoothness Priors in Computer Vision,
PAMI(17), No. 6, June 1995, pp. 576-586.
IEEE DOI Surface Reconstruction. Adaptive Smoothing. BibRef 9506

O'Sullivan, J.A.,
Roughness penalties on finite domains,
IP(4), No. 9, September 1995, pp. 1258-1268.
IEEE DOI 0402
Penalty functions in Regularization. BibRef

Lin, L.C., Kuo, C.C.J.,
On Theory and Regularization of Scale-Limited Extrapolation,
SP(54), No. 3, November 1996, pp. 225-237. 9701
BibRef

Charbonnier, P., Blanc-Féraud, L.[Laure], Aubert, G.[Gilles], Barlaud, M.,
Deterministic Edge-Preserving Regularization in Computed Imaging,
IP(6), No. 2, February 1997, pp. 298-311.
IEEE DOI 9703
BibRef
Earlier:
Two deterministic half-quadratic regularization algorithms for computed imaging,
ICIP94(II: 168-172).
IEEE DOI 9411
BibRef

Koulibaly, P.M., Charbonnier, P., Blanc-Feraud, L., Laurette, I., Darcourt, J., Barlaud, M.,
Poisson statistic and half-quadratic regularization for emission tomography reconstruction algorithm,
ICIP96(II: 729-732).
IEEE DOI 9610
BibRef

Blanc-Feraud, L., Charbonnier, P., Aubert, G., Barlaud, M.,
Nonlinear image processing: modeling and fast algorithm for regularization with edge detection,
ICIP95(I: 474-477).
IEEE DOI 9510
BibRef

Aubert, G., Barlaud, M., Blanc-Feraud, L., Charbonnier, P.,
A deterministic algorithm for edge-preserving computed imaging using Legendre transform,
ICPR94(C:188-191).
IEEE DOI 9410
BibRef

Nikolova, M., Idier, J., Mohammad-Djafari, A.,
Inversion of Large-Support Ill-Posed Linear-Operators Using a Piecewise Gaussian MRF,
IP(7), No. 4, April 1998, pp. 571-585.
IEEE DOI 9804
BibRef

Radmoser, E.[Esther], Scherzer, O.[Otmar], Weickert, J.[Joachim],
Scale-Space Properties of Nonstationary Iterative Regularization Methods,
JVCIR(11), No. 2, June 2000, pp. 96-114. 0008
BibRef
Earlier:
Scale-Space Properties of Regularization Methods,
ScaleSpace99(211-222). BibRef

Gader, P.D.[Paul D.], Khabou, M.A.[Mohamed A.], Koldobsky, A.[Alexander],
Morphological regularization neural networks,
PR(33), No. 6, June 2000, pp. 935-944.
Elsevier DOI 0004
BibRef

Raudys, S.J.[Sarunas J.],
Scaled rotation regularization,
PR(33), No. 12, December 2000, pp. 1989-1998.
Elsevier DOI 0008
BibRef

de Micheli, E.[Enrico], Viano, G.A.[Giovanni Alberto],
Probabilistic regularization in inverse optical imaging,
JOSA-A(17), No. 11, November 2000, pp. 1942-1951. 0011
BibRef

de Micheli, E.[Enrico], Viano, G.A.[Giovanni Alberto],
Inverse optical imaging viewed as a backward channel communication problem,
JOSA-A(26), No. 6, June 2009, pp. 1393-1402.
WWW Link. 0906
BibRef

Chambolle, A., Lucier, B.J.,
Interpreting translation-invariant wavelet shrinkage as a new image smoothing scale space,
IP(10), No. 7, July 2001, pp. 993-1000.
IEEE DOI 0108
BibRef

Rivera, M.[Mariano], Marroquin, J.L.[Jose L.],
Efficient half-quadratic regularization with granularity control,
IVC(21), No. 4, April 2003, pp. 345-357.
Elsevier DOI 0301
BibRef

Hinterberger, W.[Walter], Hintermüller, M.[Michael], Kunisch, K.[Karl], von Oehsen, M.[Markus], Scherzer, O.[Otmar],
Tube Methods for BV Regularization,
JMIV(19), No. 3, November 2003, pp. 219-235.
DOI Link 0310
Bounded variation regularization. BibRef

Bredies, K.[Kristian], Kunisch, K.[Karl], Pock, T.[Thomas],
Total Generalized Variation,
SIIMS(3), No. 3, 2010, pp. 492-526.
DOI Link bounded variation; total generalized variation; tensor fields; regularization; image denoising BibRef 1000

Kunisch, K.[Karl], Pock, T.[Thomas],
A Bilevel Optimization Approach for Parameter Learning in Variational Models,
SIIMS(6), No. 2, 2013, pp. 938-983.
DOI Link 1307
BibRef

Scherzer, O.[Otmar],
Taut-String Algorithm and Regularization Programs with G-Norm Data Fit,
JMIV(23), No. 2, September 2005, pp. 135-143.
Springer DOI 0505
BibRef

Fuchs, M.[Matthias], Scherzer, O.[Otmar],
Regularized Reconstruction of Shapes with Statistical a priori Knowledge,
IJCV(79), No. 2, August 2008, pp. xx-yy.
Springer DOI 0711
BibRef

Fidler, T.[Thomas], Grasmair, M.[Markus], Scherzer, O.[Otmar],
Shape Reconstruction with A Priori Knowledge Based on Integral Invariants,
SIIMS(5), No. 1 2012, pp. 726-745.
DOI Link 1208
BibRef

Vanzella, W.[Walter], Pellegrino, F.A.[Felice Andrea], Torre, V.[Vincent],
Self-Adaptive Regularization,
PAMI(26), No. 6, June 2004, pp. 804-809.
IEEE Abstract. 0404
Adapting the parameters for Mumford-Shah See also Optimal Approximations by Piecewise Smooth Functions and Variational Problems. to optimize details. BibRef

Shkvarko, Y.V.,
Unifying Regularization and Bayesian Estimation Methods for Enhanced Imaging With Remotely Sensed Data-Part I: Theory,
GeoRS(42), No. 5, May 2004, pp. 923-931.
IEEE Abstract. 0407
BibRef

Shkvarko, Y.V.,
Unifying Regularization and Bayesian Estimation Methods for Enhanced Imaging With Remotely Sensed Data-Part II: Implementation and Performance Issues,
GeoRS(42), No. 5, May 2004, pp. 932-940.
IEEE Abstract. 0407
BibRef

Shkvarko, Y.V.,
Unifying Experiment Design and Convex Regularization Techniques for Enhanced Imaging With Uncertain Remote Sensing Data: Part I: Theory,
GeoRS(48), No. 1, January 2010, pp. 82-95.
IEEE DOI 1001
BibRef

Shkvarko, Y.V.,
Unifying Experiment Design and Convex Regularization Techniques for Enhanced Imaging With Uncertain Remote Sensing Data: Part II: Adaptive Implementation and Performance Issues,
GeoRS(48), No. 1, January 2010, pp. 96-111.
IEEE DOI 1001
BibRef

Shkvarko, Y.V.[Yuriy V.], Vazquez-Bautista, R.[Rene], Villalon-Turrubiates, I.E.[Ivan E.],
Fusion of Bayesian Maximum Entropy Spectral Estimation and Variational Analysis Methods for Enhanced Radar Imaging,
ACIVS07(109-120).
Springer DOI 0708
BibRef

Shkvarko, Y.V.[Yuri V.], Netjukhailo, A.S.[Alexey S.],
Fusion of Bayesian estimation and MTF inversion techniques for improved array imaging in scattering media,
CAIP95(526-531).
Springer DOI 9509
BibRef

Shkvarko, Y.V., Leyva-Montiel, J.L., Villalon-Turrubiates, I.E.[Ivan E.],
Unifying the Experiment Design and Constrained Regularization Paradigms for Reconstructive Imaging with Remote Sensing Data,
ICIP06(3241-3244).
IEEE DOI 0610
BibRef

Viéville, T.[Thierry],
An unbiased implementation of regularization mechanisms,
IVC(23), No. 11, 1 October 2005, pp. 981-998.
Elsevier DOI 0510
BibRef

Viéville, T.[Thierry],
Biologically plausible regularization mechanisms,
INRIARR-4625, Novembre 2002.
HTML Version. 0306
BibRef

Gutierrez, J., Ferri, F.J., Malo, J.,
Regularization Operators for Natural Images Based on Nonlinear Perception Models,
IP(15), No. 1, January 2006, pp. 189-200.
IEEE DOI 0601
BibRef

Allain, M., Idier, J., Goussard, Y.,
On Global and Local Convergence of Half-Quadratic Algorithms,
IP(15), No. 5, May 2006, pp. 1130-1142.
IEEE DOI 0605
BibRef
Earlier: ICIP02(II: 833-836).
IEEE DOI 0210
BibRef

Mignotte, M.[Max],
A Segmentation-Based Regularization Term for Image Deconvolution,
IP(15), No. 7, July 2006, pp. 1973-1984.
IEEE DOI 0606
BibRef
Earlier:
An Adaptive Segmentation-Based Regularization Term for Image Restoration,
ICIP05(I: 901-904).
IEEE DOI 0512
BibRef

Mignotte, M.[Max],
A non-local regularization strategy for image deconvolution,
PRL(29), No. 16, 1 December 2008, pp. 2206-2212.
Elsevier DOI 0811
Image deconvolution or restoration; Non-local regularization; Penalized likelihood; L-curve estimation BibRef

He, L.[Lin], Burger, M.[Martin], Osher, S.J.[Stanley J.],
Iterative Total Variation Regularization with Non-Quadratic Fidelity,
JMIV(26), No. 1-2, November 2006, pp. 167-184.
Springer DOI 0701
See also Variational Problems and Partial Differential Equations on Implicit Surfaces. BibRef

Goldstein, T.[Tom], Osher, S.J.[Stanley J.],
The Split Bregman Method for L1-Regularized Problems,
SIIMS(2), No. 2, 2009, pp. 323-343. constrained optimization; L1-regularization; compressed sensing; total variation denoising
DOI Link BibRef 0900

Xu, J., Osher, S.J.[Stanley J.],
Iterative Regularization and Nonlinear Inverse Scale Space Applied to Wavelet-Based Denoising,
IP(16), No. 2, February 2007, pp. 534-544.
IEEE DOI 0702
BibRef

Grasmair, M.[Markus],
The Equivalence of the Taut String Algorithm and BV-Regularization,
JMIV(27), No. 1, January 2007, pp. 59-66.
Springer DOI 0702
BibRef

Grasmair, M.[Markus],
Locally Adaptive Total Variation Regularization,
SSVM09(331-342).
Springer DOI 0906
BibRef

Lie, J.[Johan], Nordbotten, J.M.[Jan M.],
Inverse Scale Spaces for Nonlinear Regularization,
JMIV(27), No. 1, January 2007, pp. 41-50.
Springer DOI 0702
BibRef

Laligant, O., Truchetet, F., Meriaudeau, F.,
Regularization Preserving Localization of Close Edges,
SPLetters(14), No. 3, March 2007, pp. 185-188.
IEEE DOI 0703
BibRef

Bioucas-Dias, J.M.[Jose M.], Figueiredo, M.A.T.[Mario A.T.],
A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration,
IP(16), No. 12, December 2007, pp. 2992-3004.
IEEE DOI 0711
BibRef
Earlier:
Two-Step Algorithms for Linear Inverse Problems with Non-Quadratic Regularization,
ICIP07(I: 105-108).
IEEE DOI 0709
BibRef

Afonso, M.V.[Manya V.], Bioucas-Dias, J.M.[Jose M.], Figueiredo, M.A.T.[Mario A. T.],
An augmented Lagrangian approach to linear inverse problems with compound regularization,
ICIP10(4169-4172).
IEEE DOI 1009
BibRef

Bioucas-Dias, J.M.[Jose M.], Figueiredo, M.A.T.[Mario A.T.],
An iterative algorithm for linear inverse problems with compound regularizers,
ICIP08(685-688).
IEEE DOI 0810
BibRef

Steinke, F.[Florian], Scholkopf, B.[Bernhard],
Kernels, regularization and differential equations,
PR(41), No. 11, November 2008, pp. 3271-3286.
Elsevier DOI 0808
Positive definite kernel; Differential equation; Gaussian process; Reproducing kernel Hilbert space BibRef

Steinke, F.[Florian], Hein, M.[Matthias], Scholkopf, B.[Bernhard],
Nonparametric Regression Between General Riemannian Manifolds,
SIIMS(3), No. 3, 2010, pp. 527-563.
DOI Link harmonic map; biharmonic map; Eells energy; regularized empirical risk minimization; thin-plate spline BibRef 1000

Erdem, E.[Erkut], Tari, S.[Sibel],
Mumford-Shah Regularizer with Contextual Feedback,
JMIV(33), No. 1, January 2009, pp. xx-yy.
Springer DOI 0804
BibRef

Erdem, E.[Erkut], Sancar-Yilmaz, A.[Aysun], Tari, S.[Sibel],
Mumford-Shah Regularizer with Spatial Coherence,
SSVM07(545-555).
Springer DOI 0705
BibRef

Ban, S.J., Lee, C.W., Kim, S.W.,
Adaptive Regularization Parameter for Pseudo Affine Projection Algorithm,
SPLetters(16), No. 5, May 2009, pp. 382-385.
IEEE DOI 0903
BibRef

Beck, A.[Amir], Teboulle, M.[Marc],
A Fast Iterative Shrinkage-Thresholding Algorithm For Linear Inverse Problems,
SIIMS(2), No. 1, 2009, pp. 183-202. iterative shrinkage-thresholding algorithm; deconvolution; linear inverse problem; least squares and L_1 regularization problems; optimal gradient method; global rate of convergence; two-step iterative algorithms; image deblurring
DOI Link BibRef 0900

Allard, W.K.[William K.],
Total Variation Regularization For Image Denoising, III. Examples.,
SIIMS(2), No. 2, 2009, pp. 532-568. total variation; regularization; denoising
DOI Link 0905
BibRef

Hahn, J.Y.[Joo-Young], Lee, C.O.[Chang-Ock],
A Nonlinear Structure Tensor with the Diffusivity Matrix Composed of the Image Gradient,
JMIV(34), No. 2, June 2009, pp. xx-yy.
Springer DOI 0906
Nonlinear PDE for regularization. BibRef

Mojabi, P., LoVetri, J.,
Enhancement of the Krylov Subspace Regularization for Microwave Biomedical Imaging,
MedImg(28), No. 12, December 2009, pp. 2015-2019.
IEEE DOI 0912
edge preserving. Apply to bones. BibRef

Droske, M.[Marc], Bertozzi, A.[Andrea],
Higher-Order Feature-Preserving Geometric Regularization,
SIIMS(3), No. 1, 2010, pp. 21-51.
DOI Link 1004
differential geometry; higher-order regularization; segmentation; shape optimization; image processing BibRef

Clason, C.[Christian], Jin, B.[Bangti], Kunisch, K.[Karl],
A Semismooth Newton Method for L^1 Data Fitting with Automatic Choice of Regularization Parameters and Noise Calibration,
SIIMS(3), No. 2, 2010, pp. 199-231.
DOI Link L^1 data fitting; semismooth Newton; Fenchel duality; regularization parameter; balancing principle; model function BibRef 1000

Clason, C.[Christian], Jin, B.[Bangti],
A Semismooth Newton Method for Nonlinear Parameter Identification Problems with Impulsive Noise,
SIIMS(5), No. 2 2012, pp. 505.
DOI Link 1205
BibRef

Stefan, W., Renaut, R.A., Gelb, A.,
Improved Total Variation-Type Regularization Using Higher Order Edge Detectors,
SIIMS(3), No. 2, 2010, pp. 232-251.
DOI Link total variation; higher order edge detectors BibRef 1000

Koko, J.[Jonas], Jehan-Besson, S.[Stéphanie],
An Augmented Lagrangian Method for TVg+L1-norm Minimization,
JMIV(38), No. 3, November 2010, pp. 182-196.
WWW Link. 1011
BibRef

Batard, T.[Thomas],
Clifford Bundles: A Common Framework For Image, Vector Field, and Orthonormal Frame Field Regularization,
SIIMS(3), No. 3, 2010, pp. 670-701.
DOI Link 1011
regularization; heat equations; Clifford algebras; vector bundles; differential geometry BibRef

Batard, T.[Thomas],
Heat Equations on Vector Bundles: Application to Color Image Regularization,
JMIV(41), No. 1-2, September 2011, pp. 59-85.
WWW Link. 1108
BibRef

Batard, T.[Thomas], Sochen, N.A.[Nir A.],
Polyakov Action on (rho,G)-Equivariant Functions Application to Color Image Regularization,
SSVM11(483-494).
Springer DOI 1201
BibRef

Moakher, M.[Maher], Zéraï, M.[Mourad],
The Riemannian Geometry of the Space of Positive-Definite Matrices and Its Application to the Regularization of Positive-Definite Matrix-Valued Data,
JMIV(40), No. 2, June 2011, pp. 171-187.
WWW Link. 1103
BibRef

Goldman, M.,
Continuous Primal-Dual Methods For Image Processing,
SIIMS(4), No. 1, 2011, pp. 366-385.
DOI Link 1106
primal-dual methods; total variation regularization; a posteriori estimates BibRef

Tafti, P.D.[Pouya Dehghani], Unser, M.[Michael],
On Regularized Reconstruction of Vector Fields,
IP(20), No. 11, November 2011, pp. 3163-3178.
IEEE DOI 1110
BibRef

Ramirez, I.[Ignacio], Sapiro, G.[Guillermo],
Universal Regularizers for Robust Sparse Coding and Modeling,
IP(21), No. 9, September 2012, pp. 3850-3864.
IEEE DOI 1208
BibRef

Xie, S.L.[Shou-Lie], Rahardja, S.[Susanto],
Alternating Direction Method for Balanced Image Restoration,
IP(21), No. 11, November 2012, pp. 4557-4567.
IEEE DOI 1210
balanced regularization in restoration. BibRef

Kadri-Harouna, S., Dérian, P., Héas, P., Mémin, E.,
Divergence-Free Wavelets and High Order Regularization,
IJCV(103), No. 1, May 2013, pp. 80-99.
Springer DOI 1305
BibRef

Ulfarsson, M.O., Solo, V.,
Tuning Parameter Selection for Underdetermined Reduced-Rank Regression,
SPLetters(20), No. 9, 2013, pp. 881-884.
IEEE DOI 1308
Model selection BibRef

Cremers, D.[Daniel], Strekalovskiy, E.[Evgeny],
Total Cyclic Variation and Generalizations,
JMIV(47), No. 3, November 2013, pp. 258-277.
WWW Link. 1309
BibRef
Earlier: A2, A1:
Generalized ordering constraints for multilabel optimization,
ICCV11(2619-2626).
IEEE DOI 1201
Impose some constraints on label order. BibRef
Earlier: A2, A1:
Total variation for cyclic structures: Convex relaxation and efficient minimization,
CVPR11(1905-1911).
IEEE DOI 1106
Total variation regularizer for cyclic values (angles). See also Natural Vectorial Total Variation Which Arises from Geometric Measure Theory, The. BibRef

Souiai, M., Nieuwenhuis, C.[Claudia], Strekalovskiy, E.[Evgeny], Cremers, D.[Daniel],
Convex Optimization for Scene Understanding,
GMSU13(9-14)
IEEE DOI 1403
convex programming See also Midrange Geometric Interactions for Semantic Segmentation. See also Proximity Priors for Variational Semantic Segmentation and Recognition. BibRef

Goldluecke, B.[Bastian], Strekalovskiy, E.[Evgeny], Cremers, D.[Daniel],
Tight Convex Relaxations for Vector-Valued Labeling,
SIIMS(6), No. 3, 2013, pp. 1626-1664.
DOI Link 1310
BibRef
Earlier: A2, A1, A3:
Tight convex relaxations for vector-valued labeling problems,
ICCV11(2328-2335).
IEEE DOI 1201
BibRef

Montegranario, H.[Hebert], Espinosa, J.[Jairo],
Variational Regularization of 3D Data: Experiments with MATLAB®,

Springer2014. ISBN 978-1-4939-0532-4.
WWW Link. 1404
BibRef

Guo, W., Qin, J., Yin, W.,
A New Detail-Preserving Regularization Scheme,
SIIMS(7), No. 2, 2014, pp. 1309-1334.
DOI Link 1407
BibRef

Zeng, X., Figueiredo, M.A.T.,
Decreasing Weighted Sorted L_1 Regularization,
SPLetters(21), No. 10, October 2014, pp. 1240-1244.
IEEE DOI 1407
Abstracts BibRef

Ferradans, S.[Sira], Papadakis, N.[Nicolas], Peyré, G.[Gabriel], Aujol, J.F.[Jean-François],
Regularized Discrete Optimal Transport,
SIIMS(7), No. 3, 2014, pp. 1853-1882.
DOI Link 1410
See also Synthesizing and Mixing Stationary Gaussian Texture Models. BibRef

Ferradans, S.[Sira], Papadakis, N.[Nicolas], Rabin, J.[Julien], Peyré, G.[Gabriel], Aujol, J.F.[Jean-François],
Regularized Discrete Optimal Transport,
SSVM13(428-439).
Springer DOI 1305
BibRef

Batard, T.[Thomas], Bertalmío, M.[Marcelo],
On Covariant Derivatives and Their Applications to Image Regularization,
SIIMS(7), No. 4, 2014, pp. 2393-2422.
DOI Link 1412
BibRef
And:
Duality Principle for Image Regularization and Perceptual Color Correction Models,
SSVM15(449-460).
Springer DOI 1506
BibRef

Deledalle, C.A.[Charles-Alban], Vaiter, S.[Samuel], Fadili, J.[Jalal], Peyré, G.[Gabriel],
Stein Unbiased GrAdient estimator of the Risk (SUGAR) for Multiple Parameter Selection,
SIIMS(7), No. 4, 2014, pp. 2448-2487.
DOI Link 1412
solving variational regularization of ill-posed inverse problems BibRef

Pham, D.S.[Duc-Son],
On group-wise regularization: Theory and efficient algorithms,
PR(48), No. 11, 2015, pp. 3728-3738.
Elsevier DOI 1506
lp Regularization BibRef

Yang, Z.Z.[Zhen-Zhen], Yang, Z.[Zhen],
Fast linearized alternating direction method of multipliers for the augmented L1-regularized problem,
SIViP(9), No. 7, October 2015, pp. 1601-1612.
WWW Link. 1509
BibRef

Soubies, E.[Emmanuel], Blanc-Féraud, L.[Laure], Aubert, G.[Gilles],
A Continuous Exact L_0 Penalty (CEL0) for Least Squares Regularized Problem,
SIIMS(8), No. 3, 2015, pp. 1607-1639.
DOI Link 1511
BibRef
And: Erratum: SIIMS(9), No. 1, 2016, pp. 490-494.
DOI Link 1604
BibRef

Zhang, Y.[Yong], Ye, W.Z.[Wan-Zhou],
Regularization: Convergence of iterative thresholding algorithm,
JVCIR(33), No. 1, 2015, pp. 350-357.
Elsevier DOI 1512
L_1/2 regularization BibRef

El Mouatasim, A.[Abdelkrim], Wakrim, M.[Mohammed],
Control subgradient algorithm for image L_1 regularization,
SIViP(9), No. 1 Supp, December 2015, pp. 275-283.
WWW Link. 1601
BibRef

Painsky, A., Rosset, S.,
Isotonic Modeling with Non-Differentiable Loss Functions with Application to Lasso Regularization,
PAMI(38), No. 2, February 2016, pp. 308-321.
IEEE DOI 1601
Algorithm design and analysis Code, Regularization. Implementation:
WWW Link. BibRef

Tuia, D., Flamary, R., Barlaud, M.,
Nonconvex Regularization in Remote Sensing,
GeoRS(54), No. 11, November 2016, pp. 6470-6480.
IEEE DOI 1610
Complexity theory BibRef

Cui, Z.X.[Zhuo-Xu], Fan, Q.[Qibin], Dong, Y.C.[Yi-Chuan], Liu, T.[Tong],
A nonconvex nonsmooth regularization method with structure tensor total variation,
JVCIR(43), No. 1, 2017, pp. 30-40.
Elsevier DOI 1702
Nonconvex nonsmooth regularization BibRef

Lu, J.W.[Ji-Wen], Peng, X.[Xi], Deng, W.H.[Wei-Hong], Mian, A.[Ajmal],
Regularization techniques for high-dimensional data analysis,
IVC(60), No. 1, 2017, pp. 1-3.
Elsevier DOI 1704
BibRef

Benning, M.[Martin], Gilboa, G.[Guy], Schönlieb, C.B.[Carola-Bibiane],
Learning parametrised regularisation functions via quotient minimisation,
PAMM(16), No. 1, 2016, pp. 933-936.
DOI Link 1706
BibRef

Benning, M.[Martin], Gilboa, G.[Guy], Grah, J.S.[Joana Sarah], Schönlieb, C.B.[Carola-Bibiane],
Learning Filter Functions in Regularisers by Minimising Quotients,
SSVM17(511-523).
Springer DOI 1706
BibRef

Chen, P.Y., Liu, S.,
Bias-Variance Tradeoff of Graph Laplacian Regularizer,
SPLetters(24), No. 8, August 2017, pp. 1118-1122.
IEEE DOI 1708
graph theory, signal processing, band-limited graph signals, bias-variance tradeoff, graph Laplacian regularizer, graph signal processing, mediocre regularization parameter selecting, multiple-sampled graph signals, near-optimal performance, optimal regularization parameter scaling law, random graph signals, semisupervised learning tasks, signal-to-noise ratio parameter, spectral graph properties, Eigenvalues and eigenfunctions, Laplace equations, Reactive power, Semisupervised learning, Signal to noise ratio, Symmetric matrices, Graph signal processing, mean squared error (MSE) analysis, scaling law, spectral, graph, theory BibRef


Kouw, W.M., Loog, M.,
On regularization parameter estimation under covariate shift,
ICPR16(426-431)
IEEE DOI 1705
Estimation, Parameter estimation, Pattern recognition, Risk management, Temperature measurement, Training, Training, data BibRef

Paget, M.[Mathias], Tarel, J.P.[Jean-Philippe], Caraffa, L.[Laurent],
Extending alpha-expansion to a larger set of regularization functions,
ICIP15(1051-1055)
IEEE DOI 1512
a-expansion BibRef

Kim, K.I.[Kwang In], Tompkin, J.[James], Pfister, H.[Hanspeter], Theobalt, C.[Christian],
Local high-order regularization on data manifolds,
CVPR15(5473-5481)
IEEE DOI 1510
BibRef

Gurram, P., Rao, R.,
Entropy metric regularization for computational imaging with sensor arrays,
AIPR14(1-8)
IEEE DOI 1504
Fourier transforms BibRef

Sun, B.L.[Bo-Liang], Tang, M.[Min], Li, G.H.[Guo-Hui],
Sparse Online Co-regularization Using Conjugate Functions,
ICPR14(3666-3671)
IEEE DOI 1412
Algorithm design and analysis BibRef

Gogna, A.[Anupriya], Shukla, A.[Ankita], Majumdar, A.[Angshul],
Matrix Recovery Using Split Bregman,
ICPR14(1031-1036)
IEEE DOI 1412
Matrix recovery from its lower dimensional projections. BibRef

Gong, Y.H.[Yuan-Hao], Sbalzarini, I.F.[Ivo F.],
Local weighted Gaussian curvature for image processing,
ICIP13(534-538)
IEEE DOI 1402
Approximation methods BibRef

Gilboa, G.[Guy],
Expert Regularizers for Task Specific Processing,
SSVM13(24-35).
Springer DOI 1305
BibRef

Gui, J.[Jie], Sun, Z.A.[Zhen-An], Tan, T.N.[Tie-Niu],
Regularization parameter estimation for spectral regression discriminant analysis based on perturbation theory,
ICPR12(401-404).
WWW Link. 1302
subspace learning method BibRef

Pan, B.[Binbin], Lai, J.H.[Jian-Huang], Shen, L.X.[Li-Xin],
Learning kernels from labels with ideal regularization,
ICPR12(505-508).
WWW Link. 1302
BibRef

Deledalle, C.A.[Charles-Alban], Vaiter, S.[Samuel], Peyre, G.[Gabriel], Fadili, J.[Jalal], Dossal, C.[Charles],
Unbiased risk estimation for sparse analysis regularization,
ICIP12(3053-3056).
IEEE DOI 1302
Generalized Stein Unbiased Risk Estimator (GSURE) BibRef

Rosman, G.[Guy], Wang, Y.[Yu], Tai, X.C.[Xue-Cheng], Kimmel, R.[Ron], Bruckstein, A.M.[Alfred M.],
Fast Regularization of Matrix-Valued Images,
ECCV12(III: 173-186).
Springer DOI 1210
BibRef
Earlier: Optimization11(19-43).
Springer DOI 1405
BibRef

Rabin, J.[Julien], Peyré, G.[Gabriel], Delon, J.[Julie], Bernot, M.[Marc],
Wasserstein Barycenter and Its Application to Texture Mixing,
SSVM11(435-446).
Springer DOI 1201
BibRef

Rabin, J.[Julien], Peyre, G.[Gabriel],
Wasserstein regularization of imaging problem,
ICIP11(1541-1544).
IEEE DOI 1201
BibRef

Florack, L.M.J.[Luc M.J.],
Regularization of Positive Definite Matrix Fields Based on Multiplicative Calculus,
SSVM11(786-796).
Springer DOI 1201
BibRef

Åström, F.[Freddie], Schnörr, C.[Christoph],
Double-Opponent Vectorial Total Variation,
ECCV16(II: 644-659).
Springer DOI 1611
BibRef

Yuan, J.[Jing], Schnörr, C.[Christoph], Steidl, G.[Gabriele],
Total-Variation Based Piecewise Affine Regularization,
SSVM09(552-564).
Springer DOI 0906
BibRef

Sastry, C.S.,
Regularization of Incompletely, Irregularly and Randomly Sampled Data,
ICCVGIP08(158-162).
IEEE DOI 0812
BibRef

Lin, Y.Z.[You-Zuo], Wohlberg, B.[Brendt],
Application of the UPRE Method to Optimal Parameter Selection for Large Scale Regularization Problems,
Southwest08(89-92).
IEEE DOI 0803
BibRef

Chartrand, R.[Rick],
Nonconvex Regularization for Shape Preservation,
ICIP07(I: 293-296).
IEEE DOI 0709
BibRef

Chang, H.H.[Hsun-Hsien], Moura, J.M.F.[Jose M. F.],
Classification by Cheeger Constant Regularization,
ICIP07(II: 209-212).
IEEE DOI 0709
BibRef

Lin, Z.[Zhu], Islam, M.S.,
An Adaptive Edge-Preserving Variational Framework for Color Image Regularization,
ICIP05(I: 101-104).
IEEE DOI 0512
BibRef

Chan, R.H., Ho, C.W.[Chung-Wa], Leung, C.Y.[Chun-Yee], Nikolova, M.,
Minimization of Detail-preserving Regularization Functional by Newton's Method with Continuation,
ICIP05(I: 125-128).
IEEE DOI 0512
BibRef

Zhou, D.Y.[Deng-Yong], Schölkopf, B.[Bernhard],
Regularization on Discrete Spaces,
DAGM05(361).
Springer DOI 0509
BibRef

Florack, L.M.J.[Luc M.J.],
Codomain scale space and regularization for high angular resolution diffusion imaging,
Tensor08(1-6).
IEEE DOI 0806
BibRef

Florack, L.M.J., Duits, R.[Remco], Bierkens, J.,
Tikhonov regularization versus scale space: A new result,
ICIP04(I: 271-274).
IEEE DOI 0505
BibRef

Yang, C.J.[Chang-Jiang], Duraiswami, R., Davis, L.S.,
Near-optimal regularization parameters for applications in computer vision,
ICPR02(II: 569-573).
IEEE DOI 0211
BibRef

Nikolova, M., Ng, M.,
Comparison of the main forms of half-quadratic regularization,
ICIP02(I: 349-352).
IEEE DOI 0210
BibRef

Tuysuzoglu, A.[Ahmet], Stojanovic, I.[Ivana], Castanon, D.[David], Karl, W.C.[W. Clem],
A graph cut method for linear inverse problems,
ICIP11(1913-1916).
IEEE DOI 1201
BibRef

Oraintara, S., Karl, W.C., Castanon, D.A., Nguyen, T.,
A Method for Choosing the Regularization Parameter in Generalized Tikhonov Regularized Linear Inverse Problems,
ICIP00(Vol I: 93-96).
IEEE DOI 0008
BibRef

Yang, Z.Y.[Zhi-Yong], Ma, S.D.[Song-De],
Beyond standard regularization theory,
CAIP97(289-296).
Springer DOI 9709
BibRef

Froehlinghaus, T., Buhmann, J.,
Regularizing Phase Based Stereo,
ICPR96(I: 451-455).
IEEE DOI 9608
(Rheinische Fr.-Wihelms-Univ., D) BibRef

Gunsel, B., Guzelis, C.,
Supervised learning of smoothing parameters in image restoration by regularization under cellular neural networks framework,
ICIP95(I: 470-473).
IEEE DOI 9510
BibRef

Howard, C.G., Bock, P.,
Using a hierarchical approach to avoid over-fitting in early vision,
ICPR94(A:826-829).
IEEE DOI 9410
BibRef

Szeliski, R.S.,
Regularization Uses Fractal Priors,
AAAI-87(749-754). BibRef 8700

Hummel, R., Moniot, R.,
Solving Ill-Conditioned Problems by Minimizing Equation Error,
ICCV87(527-533). BibRef 8700

Chapter on Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar continues in
Connectionist Approaches to Computer Vision .


Last update:Sep 25, 2017 at 16:36:46