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Noise-robust superresolution based on a classified dictionary

[+] Author Affiliations
Shin-Cheol Jeong

Inha University, School of Electronic Engineering, Yonghyun-dong, Nam-gu, Incheon, 402-751, Korea

Byung Cheol Song

Inha University, School of Electronic Engineering, Yonghyun-dong, Nam-gu, Incheon, 402-751, Korea

J. Electron. Imaging. 19(4), 043002 (October 29, 2010). doi:10.1117/1.3491500
History: Received January 29, 2010; Revised July 02, 2010; Accepted August 12, 2010; Published October 29, 2010; Online October 29, 2010
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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.

© 2010 SPIE and IS&T

Citation

Shin-Cheol Jeong and Byung Cheol Song
"Noise-robust superresolution based on a classified dictionary", J. Electron. Imaging. 19(4), 043002 (October 29, 2010). ; http://dx.doi.org/10.1117/1.3491500


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