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

4.1 Regularization Theory and Practice

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

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

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

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

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

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

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

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.[Pierre], Héas, P., Mémin, E.[Etienne],
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®,

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

Batard, T.[Thomas], Bertalmío, M.[Marcelo],
A Geometric Model of Brightness Perception and Its Application to Color Images Correction,
JMIV(60), No. 6, July 2018, pp. 849-881.
WWW Link. 1806
BibRef

Batard, T.[Thomas], Maldonado, E.R.[Eduard Ramon], Steidl, G.[Gabriele], Bertalmío, M.[Marcelo],
A Connection Between Image Processing and Artificial Neural Networks Layers Through a Geometric Model of Visual Perception,
SSVM19(459-471).
Springer DOI 1909
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

El Mouatasim, A.[Abdelkrim],
Control proximal gradient algorithm for image L_1 regularization,
SIViP(13), No. 6, September 2019, pp. 1113-1121.
WWW Link. 1908
BibRef

Painsky, A., Rosset, S.,
Cross-Validated Variable Selection in Tree-Based Methods Improves Predictive Performance,
PAMI(39), No. 11, November 2017, pp. 2142-2153.
IEEE DOI 1710
Analytical models, Buildings, Computational modeling, Regression tree analysis, Vegetation, Classification and regression trees, 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.B.[Qi-Bin], 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

Brinkmann, E.M.[Eva-Maria], Burger, M.[Martin], Grah, J.S.[Joana Sarah],
Unified Models for Second-Order TV-Type Regularisation in Imaging: A New Perspective Based on Vector Operators,
JMIV(61), No. 5, June 2019, pp. 571-601.
Springer DOI 1906
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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
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Brinkmann, E.M.[Eva-Maria], Burger, M.[Martin], Rasch, J.[Julian], Sutour, C.[Camille],
Bias Reduction in Variational Regularization,
JMIV(59), No. 3, November 2017, pp. 534-566.
Springer DOI 1710
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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

Åström, F.[Freddie], Schnörr, C.[Christoph],
A geometric approach for color image regularization,
CVIU(165), No. 1, 2017, pp. 43-59.
Elsevier DOI 1712
BibRef
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Double-Opponent Vectorial Total Variation,
ECCV16(II: 644-659).
Springer DOI 1611
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Yuan, J.[Jing], Schnörr, C.[Christoph], Steidl, G.[Gabriele],
Total-Variation Based Piecewise Affine Regularization,
SSVM09(552-564).
Springer DOI 0906
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Garrigos, G.[Guillaume], Rosasco, L.[Lorenzo], Villa, S.[Silvia],
Iterative Regularization via Dual Diagonal Descent,
JMIV(60), No. 2, February 2018, pp. 189-215.
Springer DOI 1802
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Bao, C.L.[Cheng-Long], Barbastathis, G.[George], Ji, H.[Hui], Shen, Z.W.[Zuo-Wei], Zhang, Z.Y.[Zheng-Yun],
Coherence Retrieval Using Trace Regularization,
SIIMS(11), No. 1, 2018, pp. 679-706.
DOI Link 1804
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Dey, P., Nag, K., Pal, T., Pal, N.R.,
Regularizing Multilayer Perceptron for Robustness,
SMCS(48), No. 8, August 2018, pp. 1255-1266.
IEEE DOI 1808
analogue circuits, mean square error methods, multilayer perceptrons, multilayer perceptron, robustness BibRef

Ringholm, T.[Torbjørn], Lazic, J.[Jasmina], Schönlieb, C.B.[Carola-Bibiane],
Variational Image Regularization with Euler's Elastica Using a Discrete Gradient Scheme,
SIIMS(11), No. 4, 2018, pp. 2665-2691.
DOI Link 1901
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Ong, F., Milanfar, P., Getreuer, P.,
Local Kernels That Approximate Bayesian Regularization and Proximal Operators,
IP(28), No. 6, June 2019, pp. 3007-3019.
IEEE DOI 1905
adaptive filters, Bayes methods, filtering theory, iterative methods, optimisation, variational techniques, Huber loss BibRef

Liu, W., Ma, X., Zhou, Y., Tao, D., Cheng, J.,
p-Laplacian Regularization for Scene Recognition,
Cyber(49), No. 8, August 2019, pp. 2927-2940.
IEEE DOI 1905
Manifolds, Laplace equations, Geometry, Eigenvalues and eigenfunctions, Standards, semi-supervised learning (SSL) BibRef

Calatroni, L., Lanza, A., Pragliola, M., Sgallari, F.,
A Flexible Space-Variant Anisotropic Regularization for Image Restoration with Automated Parameter Selection,
SIIMS(12), No. 2, 2019, pp. 1001-1037.
DOI Link 1907
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Lanza, A., Morigi, S., Selesnick, I., Sgallari, F.,
Sparsity-Inducing Nonconvex Nonseparable Regularization for Convex Image Processing,
SIIMS(12), No. 2, 2019, pp. 1099-1134.
DOI Link 1907
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Ciak, R.[René], Melching, M.[Melanie], Scherzer, O.[Otmar],
Regularization with Metric Double Integrals of Functions with Values in a Set of Vectors,
JMIV(61), No. 6, July 2019, pp. 824-848.
WWW Link. 1907
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Sun, X., Chen, B., Sun, H.,
Robust Image Compressive Sensing Based on Truncated Cauchy Loss and Nonlocal Low-Rank Regularization,
SPLetters(26), No. 12, December 2019, pp. 1842-1846.
IEEE DOI 2001
data compression, image coding, image reconstruction, impulse noise, iterative methods, quadratic programming, robustness BibRef

Parisotto, S.[Simone], Masnou, S.[Simon], Schönlieb, C.B.[Carola-Bibiane],
Higher-Order Total Directional Variation: Analysis,
SIIMS(13), No. 1, 2020, pp. 474-496.
DOI Link 2004

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Parisotto, S.[Simone], Lellmann, J.[Jan], Masnou, S.[Simon], Schönlieb, C.B.[Carola-Bibiane],
Higher-Order Total Directional Variation: Imaging Applications,
SIIMS(13), No. 4, 2020, pp. 2063-2104.
DOI Link 2012
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Bednarski, D.[Danielle], Lellmann, J.[Jan],
Inverse Scale Space Iterations for Non-convex Variational Problems Using Functional Lifting,
SSVM21(229-241).
Springer DOI 2106
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Wen, F., Ying, R., Liu, P., Qiu, R.C.,
Robust PCA Using Generalized Nonconvex Regularization,
CirSysVideo(30), No. 6, June 2020, pp. 1497-1510.
IEEE DOI 2006
Principal component analysis, Sparse matrices, Convergence, Approximation algorithms, Minimization, Mathematical model, nonconvex BibRef

Chu, Y., Chan, S.C., Zhou, Y., Wu, M.,
A New Diffusion Variable Spatial Regularized QRRLS Algorithm,
SPLetters(27), 2020, pp. 995-999.
IEEE DOI 2007
Signal processing algorithms, Covariance matrices, Probability density function, Maximum a posteriori estimation, performance analysis BibRef

Gavaskar, R.G.[Ruturaj G.], Athalye, C.D.[Chirayu D.], Chaudhury, K.N.[Kunal N.],
On Plug-and-Play Regularization Using Linear Denoisers,
IP(30), 2021, pp. 4802-4813.
IEEE DOI 2105
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Nair, P.[Pravin], Chaudhury, K.N.[Kunal N.],
Plug-and-Play Regularization Using Linear Solvers,
IP(31), 2022, pp. 6344-6355.
IEEE DOI 2210
Kernel, Image reconstruction, Convergence, Superresolution, Signal processing algorithms, Optimization, Linear systems, Krylov solver BibRef

Zhang, C.B.[Chang-Bin], Jiang, P.T.[Peng-Tao], Hou, Q.B.[Qi-Bin], Wei, Y.C.[Yun-Chao], Han, Q.[Qi], Li, Z.[Zhen], Cheng, M.M.[Ming-Ming],
Delving Deep Into Label Smoothing,
IP(30), 2021, pp. 5984-5996.
IEEE DOI
WWW Link. 2107
Code, Regularization. Training, Predictive models, Noise measurement, Smoothing methods, Tools, Robustness, Cats, Regularization, classification, soft labels, noisy labels BibRef

Liu, H.[Hao], Tai, X.C.[Xue-Cheng], Kimmel, R.[Ron], Glowinski, R.[Roland],
A Color Elastica Model for Vector-Valued Image Regularization,
SIIMS(14), No. 2, 2021, pp. 717-748.
DOI Link 2107
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Fan, B.J.[Bao-Jie], Cong, Y.[Yang], Tian, J.D.[Jian-Dong], Tang, Y.D.[Yan-Dong],
Dynamic and reliable subtask tracker with general schatten p-norm regularization,
PR(120), 2021, pp. 108129.
Elsevier DOI 2109
Reliable multi-subtask tracking, Weighted schatten -norm, Hyper-graph regularization, Decision-evaluation strategy BibRef

Kontar, R.[Raed], Raskutti, G.[Garvesh], Zhou, S.Y.[Shi-Yu],
Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach,
PAMI(43), No. 10, October 2021, pp. 3508-3522.
IEEE DOI 2109
Convolution, Gaussian processes, Covariance matrices, Computational modeling, Estimation, Numerical models, Kernel, regularization BibRef

Cohen, R.[Regev], Elad, M.[Michael], Milanfar, P.[Peyman],
Regularization by Denoising via Fixed-Point Projection (RED-PRO),
SIIMS(14), No. 3, 2021, pp. 1374-1406.
DOI Link 2110
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Manuel Vargas, V.[Víctor], Gutiérrez, P.A.[Pedro Antonio], Hervás-Martínez, C.[César],
Unimodal regularisation based on beta distribution for deep ordinal regression,
PR(122), 2022, pp. 108310.
Elsevier DOI 2112
Ordinal regression, Unimodal distribution, Convolutional network, Beta distribution, Stick-breaking BibRef

Gholinejad, S.[Saeid], Naeini, A.A.[Amin Alizadeh], Amiri-Simkooei, A.[Alireza],
Optimization of RFM Problem Using Linearly Programed L1-Regularization,
GeoRS(60), 2022, pp. 1-9.
IEEE DOI 2112
Mathematical model, Optimization, Estimation, Earth, Linear programming, Computational modeling, Satellites, RPC estimation BibRef

Fan, Q.[Qing], Liu, Y.[Yu], Yang, T.[Tao], Peng, H.[Hao],
Fast and Accurate Spectrum Estimation via Virtual Coarray Interpolation Based on Truncated Nuclear Norm Regularization,
SPLetters(29), 2022, pp. 169-173.
IEEE DOI 2202
Covariance matrices, Interpolation, Spectral analysis, Estimation, Direction-of-arrival estimation, Simulation, truncated nuclear norm BibRef

Zhou, Z.Y.[Zhi-Yong],
A Unified Framework for Constructing Nonconvex Regularizations,
SPLetters(29), 2022, pp. 479-483.
IEEE DOI 2202
Probability density function, Weibull distribution, Sparse matrices, Null space, Indexes, Urban areas, Tuning, iteratively reweighted algorithms BibRef

Sun, T.[Tao], Li, D.S.[Dong-Sheng],
General nonconvex total variation and low-rank regularizations: Model, algorithm and applications,
PR(130), 2022, pp. 108692.
Elsevier DOI 2206
Low-Rank, Total Variation, Nonconvex and nonsmooth minimization, Regularization, image restoration BibRef

Wu, C.L.[Chun-Lin], Guo, X.Y.[Xue-Yan], Gao, Y.M.[Yi-Ming], Xue, Y.H.[Yun-Hua],
A General Non-Lipschitz Infimal Convolution Regularized Model: Lower Bound Theory and Algorithm,
SIIMS(15), No. 3, 2022, pp. 1499-1538.
DOI Link 2209
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Li, Z.[Zhu], Pérez-Suay, A.[Adrián], Camps-Valls, G.[Gustau], Sejdinovic, D.[Dino],
Kernel dependence regularizers and Gaussian processes with applications to algorithmic fairness,
PR(132), 2022, pp. 108922.
Elsevier DOI 2209
Fairness, Kernel methods, Gaussian processes, Regularization, Hilbert-Schmidt independence criterion BibRef

Seyfi, M.[Mehdi], Banitalebi-Dehkordi, A.[Amin], Zhang, Y.[Yong],
Extending Momentum Contrast With Cross Similarity Consistency Regularization,
CirSysVideo(32), No. 10, October 2022, pp. 6714-6727.
IEEE DOI 2210
Semantics, Task analysis, Visualization, Representation learning, Training, Standards, Generators, Self-supervised learning, unsupervised learning BibRef

Abhishek, Kumar-Yadav, R.[Rakesh], Verma, S.[Shekhar],
Parzen Window Approximation on Riemannian Manifold,
PR(134), 2023, pp. 109081.
Elsevier DOI 2212
Parzen window, Data affinity, Graph Laplacian regularization, Manifold regularization BibRef

Zhang, H.[Hao], Qu, D.[Dan], Shao, K.[Keji], Yang, X.[Xukui],
DropDim: A Regularization Method for Transformer Networks,
SPLetters(29), 2022, pp. 474-478.
IEEE DOI 2202
Task analysis, Transformers, Smoothing methods, Semantics, Training, Neurons, Decoding, End-to-end, transformer, regularization, dropout BibRef

Liu, H.[Hao], Tai, X.C.[Xue-Cheng], Kimmel, R.[Ron], Glowinski, R.[Roland],
Elastica Models for Color Image Regularization,
SIIMS(16), No. 1, 2023, pp. 461-500.
DOI Link 2305
BibRef

Yu, D.X.[Deng-Xiu], Kang, Q.[Qian], Jin, J.W.[Jun-Wei], Wang, Z.[Zhen], Li, X.L.[Xue-Long],
Smoothing group L1/2 regularized discriminative broad learning system for classification and regression,
PR(141), 2023, pp. 109656.
Elsevier DOI 2306
Broad learning system, Discriminative, Sparsity, Smoothing group regularization, Optimization BibRef

Laville, B.[Bastien], Blanc-Feraud, L.[Laure], Aubert, G.[Gilles],
Off-the-Grid Curve Reconstruction through Divergence Regularization: An Extreme Point Result,
SIIMS(16), No. 2, 2023, pp. 867-885.
DOI Link 2306
BibRef

Chen, H.Y.[Huang-Yue], Kong, L.C.[Ling-Chen], Qu, W.T.[Wen-Tao], Xiu, X.C.[Xian-Chao],
An Enhanced Regularized Clustering Method With Adaptive Spurious Connection Detection,
SPLetters(30), 2023, pp. 1332-1336.
IEEE DOI 2310
BibRef

Zhou, X.[Xin], Liu, X.W.[Xiao-Wen], Zhang, G.[Gong], Jia, L.[Luliang], Wang, X.[Xu], Zhao, Z.Y.[Zhi-Yuan],
An Iterative Threshold Algorithm of Log-Sum Regularization for Sparse Problem,
CirSysVideo(33), No. 9, September 2023, pp. 4728-4740.
IEEE DOI 2310
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Wang, X.[Xin], Dong, X.G.[Xiao-Gang],
Time Image De-Noising Method Based on Sparse Regularization,
IJIG(23), No. 5 2023, pp. 2550009.
DOI Link 2310
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Athalye, C.D.[Chirayu D.], Chaudhury, K.N.[Kunal N.], Kumar, B.[Bhartendu],
On the Contractivity of Plug-and-Play Operators,
SPLetters(30), 2023, pp. 1447-1451.
IEEE DOI 2310
BibRef
And: Correction: SPLetters(30), 2023, pp. 1817-1817.
IEEE DOI 2401
BibRef

Ming, H.[Hao], Yang, H.[Hu],
L0 regularized logistic regression for large-scale data,
PR(146), 2024, pp. 110024.
Elsevier DOI 2311
Distributed learning, penalty, KKT conditions, Oracle property, Correlated effects BibRef

Bai, Y.[Yan], Jiao, J.[Jile], Lou, Y.H.[Yi-Hang], Wu, S.S.[Sheng-Sen], Liu, J.[Jun], Feng, X.T.[Xue-Tao], Duan, L.Y.[Ling-Yu],
Dual-Tuning: Joint Prototype Transfer and Structure Regularization for Compatible Feature Learning,
MultMed(25), 2023, pp. 7287-7298.
IEEE DOI 2311
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Ferreira, H.H.[Hermes H.], Gastal, E.S.L.[Eduardo S.L.],
Efficient 2D Tikhonov smoothness regularization with recursive filtering,
PRL(175), 2023, pp. 95-103.
Elsevier DOI 2311
Image processing, Filtering, Convolution, Frequency-domain analysis, Filtering algorithms BibRef

de los Reyes, J.C.[Juan Carlos],
Bilevel Imaging Learning Problems as Mathematical Programs with Complementarity Constraints: Reformulation and Theory,
SIIMS(16), No. 3, 2023, pp. 1655-1686.
DOI Link 2312
BibRef

Li, Y.X.[Yong-Xiang], Zhou, Q.[Qiang], Jiang, W.[Wei], Tsui, K.L.[Kwok-Leung],
Optimal Composite Likelihood Estimation and Prediction for Distributed Gaussian Process Modeling,
PAMI(46), No. 2, February 2024, pp. 1134-1147.
IEEE DOI 2401
BibRef

Cascarano, P.[Pasquale], Benfenati, A.[Alessandro], Kamilov, U.S.[Ulugbek S.], Xu, X.J.[Xiao-Jian],
Constrained Regularization by Denoising With Automatic Parameter Selection,
SPLetters(31), 2024, pp. 556-560.
IEEE DOI 2402
Standards, Image restoration, Noise reduction, Convex functions, AWGN, Signal processing algorithms, Noise measurement, discrepancy principle BibRef

Sampaio, R.A.[Raphael Araujo], Dias-Garcia, J.[Joaquim], Poggi, M.[Marcus], Vidal, T.[Thibaut],
Regularization and optimization in model-based clustering,
PR(150), 2024, pp. 110310.
Elsevier DOI 2403
Clustering, Gaussian Mixture Models, Regularization, Optimization, Hybrid Genetic Algorithm BibRef

Mondal, R.[Rahul], Pal, T.[Tandra], Dey, P.[Prasenjit],
Discriminative Regularized Input Manifold for multilayer perceptron,
PR(151), 2024, pp. 110421.
Elsevier DOI 2404
Discriminative regularization, Multilayer perceptron (MLP), Discriminative Regularized Input Manifold (DRIM), BibRef

Ghosh, A.[Avrajit], McCann, M.[Michael], Mitchell, M.[Madeline], Ravishankar, S.[Saiprasad],
Learning Sparsity-Promoting Regularizers Using Bilevel Optimization,
SIIMS(17), No. 1, 2024, pp. 31-60.
DOI Link 2404
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Goujon, A.[Alexis], Neumayer, S.[Sebastian], Unser, M.[Michael],
Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms,
SIIMS(17), No. 1, 2024, pp. 91-115.
DOI Link 2404
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Iyer, S.S.[Siddharth S.], Ong, F.[Frank], Cao, X.Z.[Xiao-Zhi], Liao, C.[Congyu], Daniel, L.[Luca], Tamir, J.I.[Jonathan I.], Setsompop, K.[Kawin],
Polynomial Preconditioners for Regularized Linear Inverse Problems,
SIIMS(17), No. 1, 2024, pp. 116-146.
DOI Link 2404
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Zhang, Z.W.[Zhong-Wang], Xu, Z.Q.J.[Zhi-Qin John],
Implicit Regularization of Dropout,
PAMI(46), No. 6, June 2024, pp. 4206-4217.
IEEE DOI 2405
Training, Artificial neural networks, Neurons, Complexity theory, Stochastic processes, Jacobian matrices, Behavioral sciences, implicit regularization BibRef

Sinha, A.[Arghya], Chaudhury, K.N.[Kunal N.],
On the Strong Convexity of PnP Regularization Using Linear Denoisers,
SPLetters(31), 2024, pp. 2790-2794.
IEEE DOI 2410
Signal processing algorithms, Convergence, Kernel, Standards, Convex functions, Superresolution, Inverse problems, Indexes, strong convexity BibRef

Zhang, M.Y.[Ming-Yan], Zhang, M.L.[Ming-Li], Zhao, F.[Feng], Zhang, F.[Fan], Liu, Y.P.[Ye-Peng], Evans, A.[Alan],
Truncated Weighted Nuclear Norm Regularization and Sparsity for Image Denoising,
ICIP23(1825-1829)
IEEE DOI 2312
BibRef

Katsuma, A.[Akari], Kyochi, S.[Seisuke], Ono, S.[Shunsuke], Selesnick, I.[Ivan],
Epigraphically-Relaxed Linearly-Involved Generalized Moreau-Enhanced Model for Layered Mixed Norm Regularization,
ICIP23(2240-2244)
IEEE DOI 2312
BibRef

Li, Z.[Zhemin], Wang, H.X.[Hong-Xia], Meng, D.Y.[De-Yu],
Regularize implicit neural representation by itself,
CVPR23(10280-10288)
IEEE DOI 2309
BibRef

Peng, Z.H.[Zheng-Hua], Luo, Y.[Yu], Chen, T.S.[Tian-Shui], Xu, K.[Keke], Huang, S.P.[Shuang-Ping],
Perception and Semantic Aware Regularization for Sequential Confidence Calibration,
CVPR23(10658-10668)
IEEE DOI 2309
BibRef

Mohammadi, K.[Kiarash], Zhao, H.[He], Zhai, M.Y.[Meng-Yao], Tung, F.[Frederick],
Ranking Regularization for Critical Rare Classes: Minimizing False Positives at a High True Positive Rate,
CVPR23(15783-15792)
IEEE DOI 2309
BibRef

Chrysos, G.G.[Grigorios G.], Wang, B.[Bohan], Deng, J.K.[Jian-Kang], Cevher, V.[Volkan],
Regularization of polynomial networks for image recognition,
CVPR23(16123-16132)
IEEE DOI 2309
BibRef

Marrie, J.[Juliette], Arbel, M.[Michael], Larlus, D.[Diane], Mairal, J.[Julien],
SLACK: Stable Learning of Augmentations with Cold-Start and KL Regularization,
CVPR23(24306-24314)
IEEE DOI 2309
BibRef

Zhu, Z.[Zeqi], Pourtaherian, A.[Arash], Waeijen, L.[Luc], Bondarev, E.[Egor], Moreira, O.[Orlando],
STAR: Sparse Thresholded Activation under partial-Regularization for Activation Sparsity Exploration,
ECV23(4554-4563)
IEEE DOI 2309
BibRef

Oliveira, H.S.[Hugo S.], Ribeiro, P.P.[Pedro P.], Oliveira, H.P.[Helder P.],
Evaluation of Regularization Techniques for Transformers-based Models,
IbPRIA23(312-319).
Springer DOI 2307
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Shi, H.[Hui], Traonmilin, Y.[Yann], Aujol, J.F.[Jean-François],
Compressive Learning of Deep Regularization for Denoising,
SSVM23(162-174).
Springer DOI 2307
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Islam, M.[Mobarakol], Glocker, B.[Ben],
Frequency Dropout: Feature-level Regularization via Randomized Filtering,
MCV22(281-295).
Springer DOI 2304
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Massart, E.[Estelle],
Orthogonal regularizers in deep learning: How to handle rectangular matrices?,
ICPR22(1294-1299)
IEEE DOI 2212
Deep learning, Training, Feedforward neural networks, Behavioral sciences BibRef

Xue, J.Q.[Jia-Qi], Zhang, B.[Bin],
Adaptive Projected Clustering with Graph Regularization,
ICPR22(3007-3013)
IEEE DOI 2212
Adaptation models, Laplace equations, Graphical models, Clustering methods, Clustering algorithms, Benchmark testing, Linear programming BibRef

Laparra, V.[Valero], Hepburn, A.[Alexander], Johnson, J.E.[J. Emmanuel], Malo, J.[Jesús],
Orthonormal Convolutions for the Rotation Based Iterative Gaussianization,
ICIP22(4018-4022)
IEEE DOI 2211
Jacobian matrices, Convolution, Independent component analysis, Transforms, Nonhomogeneous media, Iterative methods, Convolution, information theory measures BibRef

Lee, D.[Dogyoon], Lee, J.[Jaeha], Lee, J.[Junhyeop], Lee, H.[Hyeongmin], Lee, M.[Minhyeok], Woo, S.[Sungmin], Lee, S.Y.[Sang-Youn],
Regularization Strategy for Point Cloud via Rigidly Mixed Sample,
CVPR21(15895-15904)
IEEE DOI 2111
Deep learning, Measurement, Shape, Feature extraction, Distortion BibRef

Cai, L.H.[Lin-Hang], An, Z.[Zhulin], Yang, C.G.[Chuan-Guang], Xu, Y.J.[Yong-Jun],
Softer Pruning, Incremental Regularization,
ICPR21(224-230)
IEEE DOI 2105
Training, Neural networks, Information filters, Filtering theory, Softening, Convergence BibRef

Mayo, P., Holmes, R., Achim, A.,
Iterative Cauchy Thresholding: Regularisation With A Heavy-Tailed Prior,
ICIP20(2925-2929)
IEEE DOI 2011
Image reconstruction, Encoding, Optimization, Iterative algorithms, Shape, Faces, Task analysis, iterative Cauchy thresholding, ISTA, IHT, proximal operator BibRef

Osada, G.[Genki], Ahsan, B.[Budrul], Bora, R.P.[Revoti Prasad], Nishide, T.[Takashi],
Regularization with Latent Space Virtual Adversarial Training,
ECCV20(I:565-581).
Springer DOI 2011
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Izadinia, H., Garrigues, P.,
ViSeR: Visual Self-Regularization,
VL3W20(4058-4063)
IEEE DOI 2008
Visualization, Training, Genomics, Bioinformatics, Perturbation methods, Data models, Image recognition BibRef

Yun, S., Park, J., Lee, K., Shin, J.,
Regularizing Class-Wise Predictions via Self-Knowledge Distillation,
CVPR20(13873-13882)
IEEE DOI 2008
Task analysis, Error analysis, Training, Dogs, Standards, Knowledge engineering, Calibration BibRef

Yuan, L., Tay, F.E., Li, G., Wang, T., Feng, J.,
Revisiting Knowledge Distillation via Label Smoothing Regularization,
CVPR20(3902-3910)
IEEE DOI 2008
Computational modeling, Smoothing methods, Training, Neural networks, Analytical models, Reliability, Standards BibRef

Pal, A., Lane, C., Vidal, R., Haeffele, B.D.,
On the Regularization Properties of Structured Dropout,
CVPR20(7668-7676)
IEEE DOI 2008
Neurons, Training, Optimization, Biological neural networks, Approximation algorithms, Closed-form solutions BibRef

Yun, S., Han, D., Chun, S., Oh, S.J., Yoo, Y., Choe, J.,
CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features,
ICCV19(6022-6031)
IEEE DOI 2004
convolutional neural nets, feature extraction, image classification, image resolution, Robustness BibRef

Hu, M.Y.[Meng-Ying], Han, H.[Hu], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin],
Weakly Supervised Image Classification Through Noise Regularization,
CVPR19(11509-11517).
IEEE DOI 2002
BibRef

Vogt, T.[Thomas], Lellmann, J.[Jan],
Functional Liftings of Vectorial Variational Problems with Laplacian Regularization,
SSVM19(559-571).
Springer DOI 1909
BibRef

Kim, K.I.[Kwang In], Park, J.[Juhyun], Tompkin, J.[James],
High-Order Tensor Regularization with Application to Attribute Ranking,
CVPR18(4349-4357)
IEEE DOI 1812
Manifolds, Measurement, Kernel, Training, Footwear, Harmonic analysis BibRef

Aisheh, Z.A.[Zeina Abu], Bougleux, S.[Sébastien], Lézoray, O.[Olivier],
p-Laplacian Regularization of Signals on Directed Graphs,
ISVC18(650-661).
Springer DOI 1811
BibRef

Wang, D.[Dong], Wang, B.[Bin], Yao, H.X.[Hong-Xun], Liu, H.[Hong], Tombari, F.[Federico],
Local Image Descriptors with Statistical Losses,
ICIP18(1208-1212)
IEEE DOI 1809
Training, Robustness, Lighting, Feature extraction, Benchmark testing, Statistic information BibRef

Xue, F., Pan, H., Liu, X., Liu, H., Liu, J.,
Optimization of regularization parameter for sparse reconstruction based on predictive risk estimate,
ICIP17(1442-1446)
IEEE DOI 1803
Discrete wavelet transforms, Jacobian matrices, Minimization, Optimized production technology, Sparse reconstruction, predicted Stein's unbiased risk estimate (p-SURE) BibRef

Kouw, W.M., Loog, M.,
On regularization parameter estimation under covariate shift,
ICPR16(426-431)
IEEE DOI 1705
Estimation, Parameter estimation, 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
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Gurram, P., Rao, R.,
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Chapter on Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar continues in
Inverse Problems .