Ms. YingYing Fan Profile

YingYing Fan

at Tokyo Institute of Technology

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Proceedings Article | 7 March 2014 Paper

A classification-and-reconstruction approach for a single image super-resolution by a sparse representation

YingYing Fan, Masayuki Tanaka, Masatoshi Okutomi

Proceedings Volume 9023, 902312 (2014) https://doi.org/10.1117/12.2038826

KEYWORDS: Associative arrays, Super resolution, Databases, Image processing, Denoising, Image fusion, Image restoration, Data modeling, Image classification, Sensors

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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.

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