Sparse Representation for Color Image Restoration

A tutorial on support vector regression

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$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

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Matching pursuits with time-frequency dictionaries

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A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

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A Non-Local Algorithm for Image Denoising

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Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

Elad, M., & Aharon, M. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881969 · Cited by 5,321

Atomic Decomposition by Basis Pursuit

Chen, S. S., Donoho, D. L., & Saunders, M. A. (2001) · SIAM Review · doi:10.1137/s003614450037906x · Cited by 5,030

Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition

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LabelMe: A Database and Web-Based Tool for Image Annotation

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A Review of Image Denoising Algorithms, with a New One

Buades, A., Coll, B., & Morel, J. (2005) · Multiscale Modeling and Simulation · doi:10.1137/040616024 · Cited by 4,120

Heteroscedastic Sparse Representation Based Classification for Face Recognition

Zheng, H., Xie, J., & Jin, Z. (2012) · Neural Processing Letters · doi:10.1007/s11063-012-9214-4 · Cited by 13

Image classification using boosted local features with random orientation and location selection

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Building a better probabilistic model of images by factorization

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Sparse Document Image Coding for Restoration

Kumar, V., Bansal, A. K., Tulsiyan, G. H., Mishra, A., Namboodiri, A., & Jawahar, C. V. (2013) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2013.146 · Cited by 13

Reducing streak artifacts in computed tomography via sparse representation in coupled dictionaries

Karimi, D., & Ward, R. K. (2016) · Medical Physics · doi:10.1118/1.4942376 · Cited by 13

Supervised within-class-similar discriminative dictionary learning for face recognition

Xu, L., Wu, X., Chen, K., & Yao, L. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2016.04.003 · Cited by 13

Class-Adapted Blind Deblurring of Document Images

Ljubenović, M., Zhuang, L., & Figueiredo, M. A. T. (2017) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2017.123 · Cited by 13

D4L: Decentralized dynamic discriminative dictionary learning

Koppel, A., Warnell, G., Stump, E., & Ribeiro, A. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353786 · Cited by 13

Anomaly-sensitive dictionary learning for structural diagnostics from ultrasonic wavefields

Druce, J. M., Haupt, J., & Gonella, S. (2015) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2015.007048 · Cited by 13

Online Bayesian dictionary learning for large datasets

Li, L., Silva, J. A. M. D. C., Zhou, M., & Carin, L. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6288339 · Cited by 13

Low-Rank Matrix Completion to Reconstruct Incomplete Rendering Images

Liu, P., Lewis, J. P., & Rhee, T. J. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2722414 · Cited by 13

Discriminative Structured Dictionary Learning on Grassmann Manifolds and Its Application on Image Restoration

Pan, H. C., Jing, Z., Qiao, L., & Li, M. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2751585 · Cited by 13

Fast dictionary learning from incomplete data

Naumova, V., & Schnass, K. (2018) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-018-0533-0 · Cited by 13

Parallel Stochastic Successive Convex Approximation Method for Large-Scale Dictionary Learning

Koppel, A., Mokhtari, A., & Ribeiro, A. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461449 · Cited by 13

Matrix permutation meets block compressed sensing

Zhang, B., Liu, Y., Zhuang, J., Wang, K., & Cao, Y. (2019) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2019.02.023 · Cited by 13

Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse Coding

Liu, D., Sun, K., Wang, Z., Liu, R., & Zha, Z.-J. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2936135 · Cited by 13

Deterministic compressed sensing

Calderbank, A. R., Schapire, R. E., & Jafarpour, S. (2011) · Cited by 13

A speech enhancement method based on sparse reconstruction of power spectral density

Zhao, Y., Zhao, X., & Wang, B. (2014) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2013.12.007 · Cited by 13

Insect Species Recognition using Sparse Representation

Lu, A., Hou, X. W., Chen, L., & Liu, C. (2010) · British Machine Vision Conference (BMVC) · doi:10.5244/c.24.108 · Cited by 13

Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach

Qayyum, A., Malik, A. S., Saad, M. N. M., Iqbal, M., Abdullah, M. F., Rasheed, W., Abdullah, T. A. B. R., & Jafaar, M. Y. B. (2017) · Neural Computing and Applications · doi:10.1007/s00521-017-3300-5 · Cited by 13

Sparse Representations and Nonlinear Image Processing for Inverse Imaging Solutions

Ram, S. (2017) · UA Campus Repository (The University of Arizona) · Cited by 13

Knowledge Based Versus Data Based

Savadjiev, P., Reinhold, C., Martín, D. R., & Forghani, R. (2020) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2020.06.002 · Cited by 13

A Novel Reconstruction Algorithm with High Performance for Compressed Ultrafast Imaging

Shen, Q., Tian, J., & Pei, C. (2022) · Sensors · doi:10.3390/s22197372 · Cited by 12

D3CNNs: Dual Denoiser Driven Convolutional Neural Networks for Mixed Noise Removal in Remotely Sensed Images

Huang, Z., Zhu, Z., Wang, Z., Li, X., Xu, B., Zhang, Y., & Fang, H. (2023) · Remote Sensing · doi:10.3390/rs15020443 · Cited by 12

A Proximal Method for Dictionary Updating in Sparse Representations

Peng, G., & Hwang, W.-L. (2015) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2015.2434323 · Cited by 12

Hierarchized Block Wise Image Approximation by Greedy Pursuit Strategies

Rebollo‐Neira, L., Maciol, R., & Bibi, S. (2013) · IEEE Signal Processing Letters · doi:10.1109/lsp.2013.2283510 · Cited by 12

Evaluation of sparsifying algorithms for speech signals

Kassim, L. A., Khalifa, O. O., & Gunawan, T. S. (2012) · International Conference on Computer and Communication Engineering · doi:10.1109/iccce.2012.6271202 · Cited by 12

Adaptive and Robust Sparse Coding for Laser Range Data Denoising and Inpainting

Gao, Z. Y., Li, Q., Zhai, R., Shan, M., & Lin, F. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2015.2492859 · Cited by 12

Contour Detection in Colour Images Using a Neurophysiologically Inspired Model

Wang, Q., & Spratling, M. (2016) · Cognitive Computation · doi:10.1007/s12559-016-9432-6 · Cited by 12

Data-adaptive color image denoising and enhancement using graph-based filtering

Sadreazami, H., Asif, A. A., & Mohammadi, A. (2017) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2017.8050992 · Cited by 12

Lossless-constraint Denoising based Auto-encoders

Zhang, J., Zhang, Y., Bai, L., & Han, J. (2018) · Signal Processing Image Communication · doi:10.1016/j.image.2018.02.002 · Cited by 12

Hybrid higher‐order total variation model for multiplicative noise removal

Liu, P. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2018.5930 · Cited by 12

FISTA-CSNet: a deep compressed sensing network by unrolling iterative optimization algorithm

Xin, L., Wang, D., & Shi, W. (2022) · The Visual Computer · doi:10.1007/s00371-022-02583-2 · Cited by 12

Temporal Compressive Sensing for Video

Llull, P., Yuan, X., Liao, X., Yang, J., Kittle, D. S., Carin, L., Sapiro, G., & Brady, D. J. (2015) · Applied and numerical harmonic analysis · doi:10.1007/978-3-319-16042-9_2 · Cited by 12

Improving Denoising Algorithms via a Multi-scale Meta-procedure

Burger, H. C., & Stefan Harmeling, B. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23123-0_21 · Cited by 12

Generalized joint kernel regression and adaptive dictionary learning for single-image super-resolution

Huang, C., Liang, Y., Ding, X., & Fang, C. (2014) · Signal Processing · doi:10.1016/j.sigpro.2013.11.042 · Cited by 12

Identifying and Characterizing Resting State Networks in Temporally Dynamic Functional Connectomes

Zhang, X., Li, X., Jin, C., Chen, H., Li, K., Zhu, D., Jiang, X., Zhang, T., Lv, J., Hu, X., et al. (2014) · Brain Topography · doi:10.1007/s10548-014-0357-7 · Cited by 12

Tradeoffs in improved screening of lasso problems

Wang, Y., Xiang, Z. J., & Ramadge, P. J. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6638268 · Cited by 12

Image demosaicking by nonlocal adaptive thresholding

Kasar, S. M., & Ruikar, S. D. (2013) · International Conference on Signal Processing, Image Processing and Pattern Recognition (ICSIPR) · doi:10.1109/icsipr.2013.6497954 · Cited by 12

SAR Imaging With Structural Sparse Representation

Shen, F., Zhao, G., Liu, Z., Shi, G., & Lin, J. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2364294 · Cited by 12

A novel multi-feature fusion and sparse coding-based framework for image retrieval

Chen, Q., Ding, Y., Li, H., Wang, X., Wang, J., & Deng, X. (2014) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2014.6974284 · Cited by 12

Single Remote Sensing Image Super-Resolution and Denoising via Sparse Representation

Zheng, Z., Wang, B., & Sun, K. (2011) · International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping (M2RSM) · doi:10.1109/m2rsm.2011.5697420 · Cited by 12

Error concealment algorithm based on sparse optimization

Lin, T.-L., Ding, T.-L., Fan, C.-Y., & Chen, W. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-3056-9 · Cited by 12

Content-adaptive low rank regularization for image denoising

Liu, H., Zhang, X., & Xiong, R. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532928 · Cited by 12

Example-Based Image Synthesis via Randomized Patch-Matching

Ren, Y., Romano, Y., & Elad, M. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2750419 · Cited by 13

Complex impulse noise removal from color images based on super pixel segmentation

Jin, L. (2017) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2017.05.012 · Cited by 12

Handling noise in textual image resolution enhancement using online and offline learned dictionaries

Walha, R., Drira, F., Lebourgeois, F., García, C., & Alimi, A. M. (2018) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/s10032-017-0294-6 · Cited by 12

A novel framework for action recognition based on histogram of oriented gradients and sparsity-inducing shared dictionary

Jansi, R., Amutha, R., & Gokulakrishnan, A. (2017) · International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) · doi:10.1109/wispnet.2017.8299805 · Cited by 12

High-Dimensional Nonconvex Stochastic Optimization by Doubly Stochastic Successive Convex Approximation

Mokhtari, A., & Koppel, A. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.3033354 · Cited by 12

Loop Closure Detection based on Image Covariance Matrix Matching for Visual SLAM

Ying, T., Yan, H., Li, Z., Shi, K., & Feng, X. (2021) · International Journal of Control Automation and Systems · doi:10.1007/s12555-020-0730-0 · Cited by 12

Dictionaries for sparse representation and recovery of reflectances

Lansel, S., Parmar, M., & Wandell, B. A. (2009) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.813769 · Cited by 12

Dictionary Learning Based Low-Dose X-Ray CT Reconstruction

Xu, Q., Yu, H., Wang, G., & Mou, X. (2014) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789814611107_0006 · Cited by 12

ADMM for image restoration based on nonlocal simultaneous sparse Bayesian coding

Lu, X., & Lü, X. (2018) · Signal Processing Image Communication · doi:10.1016/j.image.2018.09.012 · Cited by 12

Multi-View Image Denoising Using Convolutional Neural Network

Zhou, S., Hu, Y. H., & Jiang, H. (2019) · Sensors · doi:10.3390/s19112597 · Cited by 12

Fingerprint image analysis: role of orientation patch and ridge structure dictionaries

Jain, A. K., & Cao, K. (2015) · Wiley series in probability and statistics · doi:10.1002/9781118866641.ch15 · Cited by 12

On the Application of Structured Sparse Model Selection to JPEG Compressed Images

Farinella, G. M., & Battiato, S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-20404-3_11 · Cited by 11

Image Pair Analysis With Matrix-Value Operator

Tang, Y., & Yuan, Y. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2363882 · Cited by 11

Images reconstruction using modified exemplar based method

Voronin, V., Марчук, В. И., & Egiazarian, K. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.871890 · Cited by 11

Combinative hypergraph learning for semi-supervised image classification

Wei, B., Cheng, M., Wang, C., & Li, J. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.11.028 · Cited by 11

Adapted statistical compressive sensing: Learning to sense gaussian mixture models

Duarte‐Carvajalino, J. M., Yu, G., Carin, L., & Sapiro, G. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6288708 · Cited by 11

Covariance-Assisted Matching Pursuit

Adler, A. (2015) · IEEE Signal Processing Letters · doi:10.1109/lsp.2015.2506684 · Cited by 11

An improved label fusion approach with sparse patch‐based representation for MRI brain image segmentation

Yan, M., Liu, H., Xu, X., Song, E., Qian, Y., Pan, N., Jin, R., Jin, L., Cheng, S., & Hung, C. (2017) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22207 · Cited by 11

Maximum Likelihood Estimation of Regularisation Parameters

Vidal, A. F., & Pereyra, M. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451795 · Cited by 11

Hybrid sparsity learning for image restoration: An iterative and trainable approach

Wu, F., Dong, W., Huang, T., Shi, G., Cheng, S., & Li, X. (2020) · Signal Processing · doi:10.1016/j.sigpro.2020.107751 · Cited by 12

Multispectral image denoising methods: A literature review

Reddy, P. L., & Pawar, S. (2020) · Materials Today Proceedings · doi:10.1016/j.matpr.2020.08.313 · Cited by 11

A Novel Robust Classification Method for Ground-Based Clouds

Yu, A., Tang, M., Li, G., Hou, B., Xuan, Z., Zhu, B., & Chen, T. (2021) · Atmosphere · doi:10.3390/atmos12080999 · Cited by 12

Sparse representation for heterogeneous information networks

Zhai, X., Tang, Z., Liu, Z., Zhou, W., Hu, H., Fei, G., & Hu, G. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.01.035 · Cited by 11

Image Inpainting Using Deep Learning Techniques: A Review

Haritha, L. R., & Prajith, C. A. (2023) · International Conference on Control, Communication and Computing (ICCC) · doi:10.1109/iccc57789.2023.10165271 · Cited by 11

∇-Prox: Differentiable Proximal Algorithm Modeling for Large-Scale Optimization

Lai, Z., Wei, K., Fu, Y., Härtel, P., & Heide, F. (2023) · ACM Transactions on Graphics · doi:10.1145/3592144 · Cited by 11

A Multiscale Generalized Shrinkage Threshold Network for Image Blind Deblurring in Remote Sensing

Feng, Y., Yang, Y., Fan, X., Zhang, Z., & Zhang, J. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3368760 · Cited by 11

Color video denoising using epitome and sparse coding

Lee, H. Y., Hoo, W. L., & Chan, C. S. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.08.033 · Cited by 11

Image denoising using block matching and discrete cosine transform with edge restoring

Palacios-Enriquez, A., & Ponomaryov, V. I. (2016) · International Conference on Electronics, Communications, and Computers · doi:10.1109/conielecomp.2016.7438566 · Cited by 11

Weakly supervised matrix factorization for noisily tagged image parsing

Niu, Y., Lu, Z., Huang, S., Han, P., & Wen, J.-R. (2015) · International Conference on Artificial Intelligence · Cited by 11

Learning flipping and rotation invariant sparsifying transforms

Wen, B., Ravishankar, S., & Bresler, Y. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7533082 · Cited by 11

Efficient supervised sparse analysis and synthesis operators

Sprechmann, P., Litman, R., Yakar, T. B., Bronstein, A. M., & Sapiro, G. (2013) · neural information processing systems · Cited by 11

Large-Scale Sparse Learning From Noisy Tags for Semantic Segmentation

Li, A., Lu, Z., Wang, L., Han, P., & Wen, J.-R. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2631528 · Cited by 11

A splitting method for total least squares color image restoration problem

Feiz, R., & Rezghi, M. (2017) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2017.03.001 · Cited by 11

Dictionary learning from incomplete data for efficient image restoration

Naumova, V., & Schnass, K. (2017) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2017.8081444 · Cited by 11

Dictionary Learning Phase Retrieval from Noisy Diffraction Patterns

Krishnan, J., Bioucas‐Dias, J. M., & Katkovnik, V. Y. (2018) · Sensors · doi:10.3390/s18114006 · Cited by 11

Estimation of Missing Data in Fetal Heart Rate Signals Using Shift-Invariant Dictionary

Barzideh, F., Urdal, J., Hussein, K., Engan, K., Skretting, K., Mdoe, P. F., Kamala, B. A., & Brunner, S. (2018) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2018.8553110 · Cited by 11

Retrospective correction of intensity inhomogeneity with sparsity constraints in transform-domain: Application to brain MRI

George, M. M., & Kalaivani, S. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.04.011 · Cited by 11

Slice-Based Online Convolutional Dictionary Learning

Zeng, Y., Chen, J., & Huang, G.-B. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2931914 · Cited by 11

Semi-supervised sparse representation classifier (S3RC) with deep features on small sample sized hyperspectral images

Aydemir, M. S., & Bilgin, G. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.02.092 · Cited by 11

Data-driven pre-stack AVO inversion method based on fast orthogonal dictionary

Wang, Y., Liu, Y., She, B., Hu, G., & Jin, S. Y. (2021) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.108362 · Cited by 11

Dictionary learning for sparsity-driven SAR image reconstruction

Soganlui, A., & Çetin, M. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7025339 · Cited by 11

Unsupervised segmentation of low-contrast multichannel images: discrimination of tissue components in microscopic images of unstained specimens

Kopriva, I., Hadžija, M. P., Hadžija, M., & Aralica, G. (2015) · Scientific Reports · doi:10.1038/srep11576 · Cited by 11

Sparse sample self-representation for subspace clustering

Deng, Z., Zhang, S., Yang, L., Zong, M., & Cheng, D. (2016) · Neural Computing and Applications · doi:10.1007/s00521-016-2352-2 · Cited by 11

Image restoration approach using a joint sparse representation in 3D-transform domain

Liu, S., Wu, G., Liu, H., & Zhang, X. (2016) · Digital Signal Processing · doi:10.1016/j.dsp.2016.10.008 · Cited by 11

Automatic Segmentation of Corpus Callosum in Midsagittal Based on Bayesian Inference Consisting of Sparse Representation Error and Multi-Atlas Voting

Park, G., Kwak, K., Seo, S. W., & Lee, J. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00629 · Cited by 11

Restoration of Automotive Radar Signals Under Mutual Interference by Digital In-Painting

Lee, H., Kwon, S.-Y., & Lee, S. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3427748 · Cited by 10

Adaptive prompt guided unified image restoration with latent diffusion model

Lv, X., Shao, M., Wan, Y., Qiao, Y., & Wang, C. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110267 · Cited by 11

Electron tomography image reconstruction using data-driven adaptive compressed sensing

Al–Afeef, A., Cockshott, W. P., MacLaren, I., & McVitie, S. (2015) · Scanning · doi:10.1002/sca.21271 · Cited by 10

Towards Dictionaries of Optimal Size: A Bayesian Non Parametric Approach

Dang, H.-P., & Chainais, P. (2016) · Journal of Signal Processing Systems · doi:10.1007/s11265-016-1154-1 · Cited by 10

Remote-sensing image fusion using sparse representation with sub-dictionaries

Wang, J., Peng, J., Jiang, X., Feng, X., & Zhou, J. (2017) · International Journal of Remote Sensing · doi:10.1080/01431161.2017.1302106 · Cited by 10

Demosaicing by Differentiable Deep Restoration

Tang, J., Li, J., & Tan, P. (2021) · Applied Sciences · doi:10.3390/app11041649 · Cited by 10

Structure–texture image decomposition via non-convex total generalized variation and convolutional sparse coding

Wang, C., Xu, L., & Liu, L. (2022) · The Visual Computer · doi:10.1007/s00371-021-02392-z · Cited by 10

Local Image Denoising Using RAISR

Zin, T., Seta, S., Nakahara, Y., Yamaguchi, T., & Ikehara, M. (2022) · IEEE Access · doi:10.1109/access.2022.3152219 · Cited by 10

Multi-Scale Based Approach for Denoising Real-World Noisy Image Using Curvelet Thresholding: Scope and Beyond

Panigrahi, S. K., Tripathy, S. K., Bhowmick, A., Satapathy, S. K., Barsocchi, P., & Bhoi, A. K. (2024) · IEEE Access · doi:10.1109/access.2024.3364397 · Cited by 10

Dictionary-based image denoising for dual energy computed tomography

Mechlem, K., Allner, S., Mei, K., Pfeiffer, F., & Noël, P. B. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2216749 · Cited by 10

K-LDA: An algorithm for learning jointly overcomplete and discriminative dictionaries

Golmohammady, J., Joneidi, M., Sadeghi, M., Babaie‐Zadeh, M., & Christian Jutten, B. (2014) · Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European · Cited by 10

Sparse Representation for Color Image Restoration (PREPRINT)

Mairal, J., Elad, M., & Sapiro, G. (2006) · doi:10.21236/ada478437 · Cited by 19

Sparse learned representations for image restoration

Mairal, J., Elad, M., & Sapiro, G. (2008) · Cited by 16

IASC2008: December 5-8, Yokohama, Japan Sparse learned representations for image restoration

Mairal, J., Elad, M., & Sapiro, G. (2010)

Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

Elad, M., & Aharon, M. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881969 · Cited by 5,321

Learning Multiscale Sparse Representations for Image and Video Restoration (PREPRINT)

Mairal, J., Sapiro, G., & Elad, M. (2007) · Defense Technical Information Center (DTIC) · Cited by 3