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Image resolution enhancement via image restoration using neural network

[+] Author Affiliations
Shuangteng Zhang

Eastern Kentucky University, Department of Computer Science, Richmond, Kentucky 40475

Yihong Lu

Zhejiang University of Technology, College of Information Engineering, Hangzhou, 310014, China

J. Electron. Imaging. 20(2), 023013 (May 31, 2011). doi:10.1117/1.3592523
History: Received August 26, 2010; Accepted April 29, 2011; Published May 31, 2011; Revised April 26, 2011; Online May 31, 2011
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Image super-resolution aims to obtain a high-quality image at a resolution that is higher than that of the original coarse one. This paper presents a new neural network-based method for image super-resolution. In this technique, the super-resolution is considered as an inverse problem. An observation model that closely follows the physical image acquisition process is established to solve the problem. Based on this model, a cost function is created and minimized by a Hopfield neural network to produce high-resolution images from the corresponding low-resolution ones. Not like some other single frame super-resolution techniques, this technique takes into consideration point spread function blurring as well as additive noise and therefore generates high-resolution images with more preserved or restored image details. Experimental results demonstrate that the high-resolution images obtained by this technique have a very high quality in terms of PSNR and visually look more pleasant.

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© 2011 SPIE and IS&T

Citation

Shuangteng Zhang and Yihong Lu
"Image resolution enhancement via image restoration using neural network", J. Electron. Imaging. 20(2), 023013 (May 31, 2011). ; http://dx.doi.org/10.1117/1.3592523


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Image Super-Resolution Using Deep Convolutional Networks. IEEE Trans Pattern Anal Mach Intell 2016;38(2):295-307.
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