Image resolution enhancement via image restoration using neural network

Shuangteng Zhang; Yihong Lu

doi:10.1117/1.3592523

1 April 2011 Image resolution enhancement via image restoration using neural network

Shuangteng Zhang, Yihong Lu

Author Affiliations +

Journal of Electronic Imaging, Vol. 20, Issue 2, 023013 (April 2011). https://doi.org/10.1117/1.3592523

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.

©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Shuangteng Zhang and Yihong Lu "Image resolution enhancement via image restoration using neural network," Journal of Electronic Imaging 20(2), 023013 (1 April 2011). https://doi.org/10.1117/1.3592523

Published: 1 April 2011

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