Compressed Sensing is a signal processing framework that enables the accurate reconstruction of sparse or compressible signals from a significantly reduced number of linear measurements through structured optimization, exploiting the signal's intrinsic low-dimensional structure.
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David L. Donoho · IEEE Transactions on Information Theory · 2006 · 23.1K citations
Image Super-Resolution Via Sparse Representation
John Wright, Thomas S. Huang, Yi Ma · IEEE Transactions on Image Processing · 2010 · 5.3K citations
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Stanford University
Stanford, United States
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331 publications
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Houston, United States
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367 publications
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Duke University
Durham, United States
51 h-index
301 publications
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Technion – Israel Institute of Technology
Haifa, Israel
51 h-index
196 publications
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École Polytechnique Fédérale de Lausanne
Lausanne, Switzerland
49 h-index
369 publications
21.2K citations
California Institute of Technology
Pasadena, United States
48 h-index
162 publications
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University of Wisconsin–Madison
Madison, United States
47 h-index
227 publications
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Chengdu, China
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307 publications · 48.4K citations
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184 publications · 58.6K citations
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MRM · 138 publications · 19.7K citations
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91 publications · 13.3K citations
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- IEEE Signal Processing Letters
144 publications · 7.9K citations
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