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Proceedings Paper

Sparse representation using multiple dictionaries for single image super-resolution
Author(s): Yih-Lon Lin; Chung-Ming Sung; Yu-Min Chiang
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Paper Abstract

New algorithms are proposed in this paper for single image super-resolution using multiple dictionaries based on sparse representation. In the proposed algorithms, a classifier is constructed which is based on the edge properties of image patches via the two lowest discrete cosine transformation (DCT) coefficients. The classifier partitions all training patches into three classes. Training patches from each of the three classes can then be used for the training of the corresponding dictionary via the K-SVD (singular value decomposition) algorithm. Experimental results show that the high resolution image quality using the proposed algorithms is better than that using the traditional bi-cubic interpolation and Yang’s method.

Paper Details

Date Published: 4 March 2015
PDF: 6 pages
Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 944316 (4 March 2015); doi: 10.1117/12.2179097
Show Author Affiliations
Yih-Lon Lin, I-Shou Univ. (Taiwan)
Chung-Ming Sung, I-Shou Univ. (Taiwan)
Yu-Min Chiang, I-Shou Univ. (Taiwan)


Published in SPIE Proceedings Vol. 9443:
Sixth International Conference on Graphic and Image Processing (ICGIP 2014)
Yulin Wang; Xudong Jiang; David Zhang, Editor(s)

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