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- The convolutional sparse coding paradigm is an extension of the global sparse coding model, in which a redundant dictionary is modeled as a concatenation of circulant matrices. While the global sparsity constraint describes signal as a linear combination of a few atoms in the redundant dictionary , usually expressed as for a sparse vector , the alternative dictionary structure adopted by the convolutional sparse coding model allows the sparsity prior to be applied locally instead of globally: independent patches of are generated by "local" dictionaries operating over stripes of . The local sparsity constraint allows stronger uniqueness and stability conditions than the global sparsity prior, and has shown to be a versatile tool for inverse problems in fields such as image understanding and computer vision. Also, a recently proposed multi-layer extension of the model has shown conceptual benefits for more complex signal decompositions, as well as a tight connection the convolutional neural networks model, allowing a deeper understanding of how the latter operates. (en)
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- 39188 (xsd:nonNegativeInteger)
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- The convolutional sparse coding paradigm is an extension of the global sparse coding model, in which a redundant dictionary is modeled as a concatenation of circulant matrices. While the global sparsity constraint describes signal as a linear combination of a few atoms in the redundant dictionary , usually expressed as for a sparse vector , the alternative dictionary structure adopted by the convolutional sparse coding model allows the sparsity prior to be applied locally instead of globally: independent patches of are generated by "local" dictionaries operating over stripes of . (en)
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- Convolutional sparse coding (en)
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