{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/81183"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/81183","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Blind Multichannel Image Deconvolution and Optimum Sparse Approximations","abstract":"The second problem is one of computing maximally sparse elements of a convex, compact set. This problem arises in a wide range of engineering applications, including regularization of ill-posed problems, design of digital filters with few non-zero coefficients and the computation of sparse approximate solutions to inverse problems. Because the problem is N-P complete, there exists a need to develop heuristic techniques that work well for specific problems. Our contribution is the development of a new class of iterative algorithms for identifying sparse elements of the convex and compact set. We show that the algorithm has good convergence properties through a detailed theoretical analysis and demonstrate its performance on some examples.","abstract_html":"The second problem is one of computing maximally sparse elements of a convex, compact set. This problem arises in a wide range of engineering applications, including regularization of ill-posed problems, design of digital filters with few non-zero coefficients and the computation of sparse approximate solutions to inverse problems. Because the problem is N-P complete, there exists a need to develop heuristic techniques that work well for specific problems. Our contribution is the development of a new class of iterative algorithms for identifying sparse elements of the convex and compact set. 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This problem arises in a wide range of engineering applications, including regularization of ill-posed problems, design of digital filters with few non-zero coefficients and the computation of sparse approximate solutions to inverse problems. Because the problem is N-P complete, there exists a need to develop heuristic techniques that work well for specific problems. Our contribution is the development of a new class of iterative algorithms for identifying sparse elements of the convex and compact set. We show that the algorithm has good convergence properties through a detailed theoretical analysis and demonstrate its performance on some examples.","Made available in DSpace on 2015-09-25T20:09:58Z (GMT). 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