Noise-robust superresolution based on a classified dictionary

Shin-Cheol Jeong; Byung Cheol Song

doi:10.1117/1.3491500

1 October 2010 Noise-robust superresolution based on a classified dictionary

Shin-Cheol Jeong, Byung Cheol Song

Author Affiliations +

Journal of Electronic Imaging, Vol. 19, Issue 4, 043002 (October 2010). https://doi.org/10.1117/1.3491500

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.

©(2010) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Shin-Cheol Jeong and Byung Cheol Song "Noise-robust superresolution based on a classified dictionary," Journal of Electronic Imaging 19(4), 043002 (1 October 2010). https://doi.org/10.1117/1.3491500

Published: 1 October 2010

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available