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Journal of Electronic Imaging

Noise-robust superresolution based on a classified dictionary
Author(s): Shin-Cheol Jeong; Byung Cheol Song
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Paper Abstract

Conventional learning-based superresolution algorithms tend to boost noise components existing in input images because the algorithms are usually learned in a noise-free environment. Even though a specific noise reduction algorithm is applied to noisy images prior to superresolution, visual quality degradation is inevitable due to the mismatch between noise-free images and denoised images. Accordingly, we present a noise-robust superresolution algorithm that overcomes this problem. In the learning phase, a dictionary classified according to noise level is constructed, and then a high-resolution image is synthesized using the dictionary in the inference phase. Experimental results show that the proposed algorithm outperforms existing algorithms for various noisy images.

Paper Details

Date Published: 1 October 2010
PDF: 10 pages
J. Electron. Imag. 19(4) 043002 doi: 10.1117/1.3491500
Published in: Journal of Electronic Imaging Volume 19, Issue 4
Show Author Affiliations
Shin-Cheol Jeong, Inha Univ. (Korea, Republic of)
Byung Cheol Song, Inha Univ. (Korea, Republic of)

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