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Proceedings Paper

A classification-and-reconstruction approach for a single image super-resolution by a sparse representation
Author(s): YingYing Fan; Masayuki Tanaka; Masatoshi Okutomi
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Paper Abstract

A sparse representation is known as a very powerful tool to solve image reconstruction problem such as denoising and the single image super-resolution. In the sparse representation, it is assumed that an image patch or data can be approximated by a linear combination of a few bases selected from a given dictionary. A single overcomplete dictionary is usually learned with training patches. Dictionary learning methods almost are concerned about building a general over-complete dictionary on the assumption that the bases in dictionary can represent everything. However, using more appropriate dictionary, the sparse representation of patch can obtain better results. In this paper, we propose a classification-and-reconstruction approach with multiple dictionaries. Before learning dictionary for reconstruction, some representative bases can be used to classify all training patches from database and multiple dictionaries for reconstruction can be learned by classified patches respectively. In reconstruction phase, the patch of input image can be classified and the adaptive dictionary can be selected to use. We demonstrate that the proposed classification-and-reconstruction approach outperforms existing sparse representation with the single dictionary.

Paper Details

Date Published: 7 March 2014
PDF: 6 pages
Proc. SPIE 9023, Digital Photography X, 902312 (7 March 2014); doi: 10.1117/12.2038826
Show Author Affiliations
YingYing Fan, Tokyo Institute of Technology (Japan)
Masayuki Tanaka, Tokyo Institute of Technology (Japan)
Masatoshi Okutomi, Tokyo Institute of Technology (Japan)


Published in SPIE Proceedings Vol. 9023:
Digital Photography X
Nitin Sampat; Radka Tezaur; Sebastiano Battiato; Boyd A. Fowler, Editor(s)

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