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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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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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