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Journal of Electronic Imaging

Image resolution enhancement via image restoration using neural network
Author(s): Shuangteng Zhang; Yihong Lu
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Paper Abstract

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.

Paper Details

Date Published: 1 April 2011
PDF: 10 pages
J. Electron. Imag. 20(2) 023013 doi: 10.1117/1.3592523
Published in: Journal of Electronic Imaging Volume 20, Issue 2
Show Author Affiliations
Shuangteng Zhang, Eastern Kentucky Univ. (United States)
Yihong Lu, Zhejiang Univ. of Technology (China)

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