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Sparse Representation for Color Image Restoration
Open accessAbstract
Sparse representations of signals have drawn considerable interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. In particular, the design of well adapted dictionaries for images has been a major challenge. The K-SVD has been recently proposed for this task and shown to perform very well for various grayscale image processing tasks. In this paper, we address the problem of learning dictionaries for color images and extend the K-SVD-based grayscale image denoising algorithm that appears in. This work puts forward ways for handling nonhomogeneous noise and missing information, paving the way to state-of-the-art results in applications such as color image denoising, demosaicing, and inpainting, as demonstrated in this paper.
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Cites
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,723
$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
Aharon, M., Elad, M., & Bruckstein, A. M. (2006) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.881199 · Cited by 9,464
Self-citationMatching pursuits with time-frequency dictionaries
Mallat, S., & Zhang, Z. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.258082 · Cited by 9,023
Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937655 · Cited by 8,216
A Non-Local Algorithm for Image Denoising
Buades, A., Coll, B., & Morel, J.-M. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.38 · Cited by 7,616
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
Self-citationAtomic Decomposition by Basis Pursuit
Chen, S. S., Donoho, D. L., & Saunders, M. A. (2001) · SIAM Review · doi:10.1137/s003614450037906x · Cited by 5,030
Pati, Y., Rezaiifar, R., & Krishnaprasad, P. (1993) · Proceedings of 27th Asilomar Conference on Signals, Systems and Computers · doi:10.1109/acssc.1993.342465 · Cited by 4,928
LabelMe: A Database and Web-Based Tool for Image Annotation
Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0090-8 · Cited by 4,210
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
Flagged first, then by citations
Research works printed but not linked: 4
- tappen, 2003, exploiting the sparse derivative prior for super-resolution and image demosaicing, IEEE Workshop Statistical and Computational Theories of Vision
- 10.1109/TIP.2004.838697 · DOI
- mallat, 1999, A Wavelet Tour of Signal Processing (Wavelet Analysis & Its Applications)
- do, 2003, Contourlets Beyond Wavelets
Cited by
Sorted by citations
Items 801–900 of 1,000
Chen, Z., Lu, J., Feng, J., & Zhou, J. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2705918 · Cited by 10
Scientific data interpolation with low dimensional manifold model
Zhu, W., Wang, B., Barnard, R., Hauck, C. D., Jenko, F., & Osher, S. (2017) · Journal of Computational Physics · doi:10.1016/j.jcp.2017.09.048 · Cited by 10
Multispectral Image Denoising via Nonlocal Multitask Sparse Learning
Fan, Y., Huang, T., Zhao, X.-L., Deng, L.-J., & Fan, S. (2018) · Remote Sensing · doi:10.3390/rs10010116 · Cited by 10
Sparse representation based facial image compression via multiple dictionaries and separated ROI
Taheri, A. M., & Mahdavi‐Nasab, H. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6197-9 · Cited by 10
Simultaneous Super-Resolution and Cross-Modality Synthesis in Magnetic Resonance Imaging
Huang, Y., Shao, L., & Frangi, A. F. (2019) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-13969-8_21 · Cited by 10
Hu, X., & Liu, X. (2020) · Sensors · doi:10.3390/s20113041 · Cited by 10
Boundary precedence image inpainting method based on Self-organizing Maps
Pen, H., Wang, Q., & Wang, Z. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106722 · Cited by 10
Fusing Deep Features by Kernel Collaborative Representation for Remote Sensing Scene Classification
Chen, X., Ma, M., Li, Y., & Cheng, W. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3130073 · Cited by 10
Li, J., Liu, S., Peng, Y., Tang, J., Zhang, X., & Li, Y. (2022) · Journal of Applied Geophysics · doi:10.1016/j.jappgeo.2022.104577 · Cited by 10
Bertalmı́o, M., Caselles, V., Masnou, S., & Sapiro, G. (2014) · Springer eBooks · doi:10.1007/978-0-387-31439-6_249 · Cited by 10
Self-citationTask-driven dictionary learning for inpainting
Hu, H., Wohlberg, B. E., & Chartrand, R. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854260 · Cited by 10
Removal of random-valued impulse noise using sparse representation
Deka, B., & Bora, P. K. (2011) · National Conference on Communications · doi:10.1109/ncc.2011.5734762 · Cited by 10
Rain streak removal by multi-frame-based anisotropic filtering
Wang, C., Shen, M., & Yao, C. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-015-3195-z · Cited by 10
Moving Object Detection with Fixed Camera and Moving Camera for Automated Video Analysis
Shahare, D., & Shende, R. (2014) · International Journal of Computer Applications Technology and Research · doi:10.7753/ijcatr0305.1001 · Cited by 10
Fast sparsity adaptive multipath matching pursuit for compressed sensing problems
Zhang, X., Du, H., Qiu, B., & Chen, S. (2017) · Journal of Electronic Imaging · doi:10.1117/1.jei.26.3.033007 · Cited by 10
Sparse Technique for Images Corrupted by Mixed Gaussian-Impulsive Noise
Palacios-Enriquez, A., Ponomaryov, V. I., Reyes-Reyes, R., & Sadovnychiy, S. (2018) · Circuits Systems and Signal Processing · doi:10.1007/s00034-018-0820-x · Cited by 10
Deep Neural Networks for In Situ Hybridization Grid Completion and Clustering
Li, Y., Huang, H., Chen, H., & Liu, T. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2864262 · Cited by 10
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Wang, P., Li, Y.-B., Wang, M., Qin, X., & Liu, L. (2019) · Journal of Central South University · doi:10.1007/s11771-019-4032-8 · Cited by 10
Structured Sparse Recovery via Convex OptimizationPreprint
Elhamifar, E., & Vidal, R. (2011) · arXiv (Cornell University) · Cited by 10
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Kong, Z., Yang, X., & He, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.03462 · Cited by 16
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Chouvardas, S., Kopsinis, Y., & Theodoridis, S. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178580 · Cited by 8
Qian, Y., Ye, M., & Wang, Q. (2012) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2012.6350674 · Cited by 9
Learning auxiliary dictionaries for undersampled face recognition
Wei, C.-P., & Wang, Y.-C. F. (2013) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2013.6607549 · Cited by 9
Conformal prediction based active learning by linear regression optimization
Matiz, S., & Barner, K. E. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.018 · Cited by 9
Saturation-Value Blind Color Image Deblurring with Geometric Spatial-Feature Prior
Zhang, H., Fang, Y., Wong, H. S., Li, L., & Zeng, T. (2023) · Communications in Computational Physics · doi:10.4208/cicp.oa-2022-0226 · Cited by 9
Survey on Image Saliency Detection Methods
Runxin, M., Yu, Y., & Xiaomin, Y. (2015) · International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) · doi:10.1109/cyberc.2015.98 · Cited by 9
Motion recognition and synthesis based on 3D sparse representation
Xiang, J., & Liang, R. (2014) · Signal Processing · doi:10.1016/j.sigpro.2014.08.020 · Cited by 9
Self-learning-based signal decomposition for multimedia applications: A review and comparative study
Kang, L., Yeh, C., Chen, D.-Y., & Lin, C.-T. (2014) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2014.7041778 · Cited by 9
Julazadeh, A., Alirezaie, J., & Babyn, P. (2012) · International Conference on Information Science, Signal Processing and their Applications (ISSPA) · doi:10.1109/isspa.2012.6310680 · Cited by 9
Markov Random Field based Image Restoration with aid of Local and Global Features
George, A., Rajakumar, B. R., & Suresh, B. S. (2012) · International Journal of Computer Applications · doi:10.5120/7369-0137 · Cited by 9
Inpainting Color Images In Learned Dictionary
Cichocki, A., Filipović, M., & Kopriva, I. (2012) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.42760 · Cited by 9
Robust Estimation Approach for Nonlocal-means Denoising Based On Structurally Similar Patches
Mathew, A. T. (2009) · Cited by 9
Filipović, M., & Kopriva, I. (2011) · Inverse Problems and Imaging · doi:10.3934/ipi.2011.5.815 · Cited by 9
Ke, L., You, X., & Wan, G. (2015) · International Conference on Virtual Reality and Visualization · doi:10.1109/icvrv.2015.35 · Cited by 9
Hierarchical sparse coding from a Bayesian perspective
Zhang, Y., Xiang, M., & Yang, B. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.06.076 · Cited by 9
3D Shape Processing by Convolutional Denoising Autoencoders on Local Patches
Sarkar, K., Varanasi, K., & Stricker, D. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00213 · Cited by 9
Hyperspectral image clustering via sparse dictionary‐based anchored regression
Huang, N., & Xiao, L. (2018) · IET Image Processing · doi:10.1049/iet-ipr.2018.5421 · Cited by 9
Photorealistic Face Completion with Semantic Parsing and Face Identity-Preserving Features
Ma, R., Hu, H., Wang, W., Xu, J. D., & Li, Z. (2019) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3300940 · Cited by 9
Abundance Estimation Using Discontinuity Preserving and Sparsity-Induced Priors
Patel, J. R., Joshi, M. V., & Bhatt, J. S. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2906756 · Cited by 9
Experimental study on imaging and image deconvolution of a diffractive telescope system
Yang, J., Wang, S., Wen, L., Yang, P., Yang, W., Guan, C., & Xu, B. (2019) · Applied Optics · doi:10.1364/ao.58.009059 · Cited by 9
Network sparse representation: Decomposition, dimensionality-reduction and reconstruction
Zhai, X., Zhou, W., Fei, G., Lu, C., & Hu, G. (2020) · Information Sciences · doi:10.1016/j.ins.2020.02.022 · Cited by 9
An Infrared Small Target Detection Algorithm Based on Peak Aggregation and Gaussian Discrimination
Jiang, Y., Dong, L., Chen, Y., & Xu, W. (2020) · IEEE Access · doi:10.1109/access.2020.3000227 · Cited by 9
Direct Semantic Analysis for Social Image Classification
Lu, Z., Wang, L., & Wen, J.-R. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8899 · Cited by 9
Learning dictionary from signals under global sparsity constraint
Meng, D., Zhao, Q., Leung, Y., & Xu, Z. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.03.028 · Cited by 9
E-cient Implementation of the K-SVD Algorithm and the Batch-OMP Method
Rubinstein, R., Zibulevsky, M., & Elad, M. (2008) · Cited by 9
Self-citationWang, Z., Xie, J., Zhou, G., Ji, Z., & Quan, T. (2015) · IET Radar Sonar & Navigation · doi:10.1049/iet-rsn.2014.0441 · Cited by 9
Generalized System Identification with Stable Spline Kernels
Aravkin, A. Y., Burke, J. V., & Pillonetto, G. (2018) · SIAM Journal on Scientific Computing · doi:10.1137/16m1070517 · Cited by 9
Symmetry‐Aware Mesh Segmentation into Uniform Overlapping Patches
Dessein, A., Smith, W. A. P., Wilson, R. C., & Hancock, E. R. (2016) · Computer Graphics Forum · doi:10.1111/cgf.12997 · Cited by 9
Li, Y., Chen, H., Jiang, X., Li, X., Lv, J., Li, M., Peng, H., Tsien, J. Z., & Liu, T. (2017) · Neuroinformatics · doi:10.1007/s12021-017-9333-1 · Cited by 9
Local sparse representation for astronomical image denoising
Yang, A., Lu, M., Teng, S., & Sun, J. (2013) · Journal of Central South University · doi:10.1007/s11771-013-1789-z · Cited by 9
International Journal of Ethics in Engineering & Management Education
vision-i, P. (2014) · ADVANCED SCIENCES INDEX · Cited by 9
Group Sparsity Residual Constraint for Image DenoisingPreprint
Zha, Z., Zhang, X., Wang, Q., Tang, L., & Liu, X. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.00297 · Cited by 9
Challenges in recommender systems : scalability, privacy, and structured recommendations
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Su, S., & Yang, J. (2024) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2024.108503 · Cited by 8
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Mandal, S., Thavalengal, S., & Sao, A. K. (2015) · Pattern Analysis and Applications · doi:10.1007/s10044-015-0512-0 · Cited by 8
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Multi-dimensional signal approximation with sparse structured priors using split Bregman iterations
Isaac, Y., Barthélemy, Q., Gouy‐Pailler, C., Sébag, M., & Jamal Atif, C. (2016) · Signal Processing · doi:10.1016/j.sigpro.2016.07.013 · Cited by 8
Adaptive dictionaries for compressive imaging
Aghagolzadeh, M., & Radha, H. (2013) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2013.6737070 · Cited by 8
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Gangeh, M. J., Bedawi, S. M., Ghodsi, A., & Karray, F. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-41501-7_2 · Cited by 8
Dictionary Learning on Grassmann Manifolds
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Guo, M., Wang, Z., & Yang, N. (2017) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-017-0327-0 · Cited by 8
Dash, P. P., & Patra, D. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-017-0663-5 · Cited by 8
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Gao, C., Liu, F., & Yan, H. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-200554 · Cited by 8
Local Smoothing Neighborhood Filters
Morel, J., Buades, A., & Coll, T. (2015) · Springer eBooks · doi:10.1007/978-1-4939-0790-8_26 · Cited by 8
Digital Analysis of Changes in Chronic Wounds through Image Processing
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Al–Afeef, A., Bobynko, J., Cockshott, W. P., Craven, A. J., Zuazo, I., Barges, P., & MacLaren, I. (2016) · Ultramicroscopy · doi:10.1016/j.ultramic.2016.08.004 · Cited by 8
Retracted: Simultaneous multi-component seismic denoising and reconstruction via K-SVD
Hou, S., Zhang, F., Li, X., Zhao, Q., & Dai, H. (2017) · Journal of Geophysics and Engineering · doi:10.1088/1742-2140/aa953a · Cited by 8
RetractedWeighted lp Norm Sparse Error Constraint Based ADMM for Image Denoising
Xu, J., Wang, N., Xu, Z., & Xu, K. (2019) · Mathematical Problems in Engineering · doi:10.1155/2019/1262171 · Cited by 8
Gao, J., Yu, Z., Nie, K., & Xu, J. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2997955 · Cited by 8
A Cost-Sensitive Sparse Representation Based Classification for Class-Imbalance Problem
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Average-face-based virtual inpainting for severely damaged statues of Dazu Rock Carvings
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Self-Supervised Fast Adaptation for Denoising via Meta-LearningPreprint
Lee, S., Cho, D., Kim, J., & Kim, T. H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.02899 · Cited by 8
A syncretic representation for image classification and face recognition
Ma, Z., Liu, Q., Sun, K., & Zhan, S. (2016) · CAAI Transactions on Intelligence Technology · doi:10.1016/j.trit.2016.08.003 · Cited by 7
Weighted Nuclear Norm Minimization-Based Regularization Method for Image Restoration
Huang, Y., & Yan, H.-Y. (2020) · Communications on Applied Mathematics and Computation · doi:10.1007/s42967-020-00076-4 · Cited by 7
Learning low-rank latent mesoscale structures in networks
Lyu, H., Kureh, Y. H., Vendrow, J., & Porter, M. A. (2024) · Nature Communications · doi:10.1038/s41467-023-42859-2 · Cited by 12
The generalized lasso is reducible to a subspace constrained lasso
Xu, H., Eis, D. J., & Ramadge, P. J. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6638262 · Cited by 7
Image segmentation and inpainting using hierarchical level set and texture mapping
Du, X., Cho, D., & Bui, T. D. (2010) · Signal Processing · doi:10.1016/j.sigpro.2010.09.002 · Cited by 7
Innovative sparse representation algorithms for robust face recognition
Qui, H., Pham, D.-S., Venkatesh, S., Lai, J., & Liu, W. (2011) · eSpace (Curtin University) · Cited by 7
Image and Video Restoration and Enhancement via Sparse Representation
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Multicomponent seismic reconstruction via modified inner product operator and weighted k-SVD
Hou, S., Zhang, F., & Li, X. (2016) · Second International Meeting for Applied Geoscience & Energy · doi:10.1190/segam2016-13876928.1 · Cited by 7
Predictive high-level feature representation based on dictionary learning
Phaisangittisagul, E., Thainimit, S., & Chen, W. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.10.019 · Cited by 7
Spontaneous Facial Expression Recognition using Sparse Representation
Chanti, D. A., & Caplier, A. (2017) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0006118000640074 · Cited by 7
Data Driven Adaptive Localization With Applications To Ensemble-Based 4D Seismic History Matching
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Model-Based Iterative Restoration for Binary Document Image Compression with Dictionary Learning
Guo, Y., Lu, C. Y., Allebach, J. P., & Bouman, C. A. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.72 · Cited by 7
Efficient learning of dictionaries with low-rank atoms
Ravishankar, S., Moore, B. E., Nadakuditi, R. R., & Fessler, J. A. (2016) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2016.7905836 · Cited by 7
Hypercomplex algebras for dictionary learning
Lazendić, S., Pižurica, A., & Bie, H. D. (2018) · Ghent University Academic Bibliography (Ghent University) · Cited by 7
A Novel Approach of K-SVD-Based Algorithm for Image Denoising
Madhu, G., & Rudra, S. (2018) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-5225-6316-7.ch007 · Cited by 7
Construction of Diverse Image Datasets From Web Collections With Limited Labeling
Mithun, N. C., Panda, R., & Roy–Chowdhury, A. K. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2898899 · Cited by 7
Metadata
- Authors
- Mairal, J., Elad, M., & Sapiro, G.
- Affiliations
- University of Minnesota (US)
- Technion – Israel Institute of Technology (IL)
- Venue
- IEEE Transactions on Image Processing
- Published
- 2008-01
- Volume
- 17
- Issue
- 1
- Pages
- 53-69
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2114122776
- Cited by
- 1,716
- References
- 60
- Citations by year
- 2026: 11 · 2025: 30 · 2024: 45 · 2023: 59 · 2022: 70 · 2021: 75 · 2020: 98 · 2019: 114 · 2018: 130 · 2017: 145 · 2016: 138 · 2015: 182 · 2014: 167 · 2013: 138 · 2012: 105
- FWCI
- 45.96
- Type
- journal article
- Language
- en
- Open access
- green
- purl.umn.edu
- Locations
- IEEE Transactions on Image Processing (publishedVersion)
- PubMed (publishedVersion)
- University of Minnesota Digital Conservancy (University of Minnesota) (submittedVersion)
- Funders
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Computer Science › Signal Processing › Sparse and Compressive Sensing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Data Compression Techniques
- Keywords
- sparse representation, color image restoration, dictionary learning, K-SVD, color image denoising, demosaicing, image inpainting, missing information, redundant dictionaries
- MeSH terms
- Algorithms, Artificial Intelligence, Color, Colorimetry, Image Enhancement, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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