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The new dictionary is itself a small image, such that every patch in it (in varying location and size) is a possible atom in the representation. We refer to this as the image-signature-dictionary (ISD) and show how it can be trained from image examples. This structure extends the well-known image and video epitomes, as introduced by Jojic, Frey, and Kannan [in Proceedings of the IEEE International Conference on Computer Vision, 2003, pp. 34\u201341] and Cheung, Frey, and Jojic [in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, pp. 42\u201349], by replacing a probabilistic averaging of patches with their sparse representations. The ISD enjoys several important features, such as shift and scale flexibilities, and smaller memory and computational requirements, compared to the classical dictionary approach. As a demonstration of these benefits, we present high-quality image denoising results based on this new model.<\/jats:p>","DOI":"10.1137\/07070156x","type":"journal-article","created":{"date-parts":[[2008,10,29]],"date-time":"2008-10-29T13:45:01Z","timestamp":1225287901000},"page":"228-247","source":"Crossref","is-referenced-by-count":118,"title":["Sparse and Redundant Modeling of Image Content Using an Image-Signature-Dictionary"],"prefix":"10.1137","volume":"1","author":[{"given":"Michal","family":"Aharon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Elad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"351","published-online":{"date-parts":[[2008,7,30]]},"reference":[{"key":"R1","unstructured":"M. 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