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Proceedings Paper

MLP neural network super-resolution restoration for the undersampled low-resolution image
Author(s): Binghua Su; Weiqi Jin; LiHong Niu; Guangrong Liu
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Paper Abstract

It is difficult to achieve restoration of high frequency information by the traditional algorithms using an undersampled and degraded low-resolution image. Nonlinear algorithms provide a better solution to above problem. As a nonlinear and real-time processing method, a MLP neural network super-resolution restoration for the undersampled and degraded low-resolution image is proposed. Experimental results demonstrate that the proposed approach can achieve super-resolution and a good restored image.

Paper Details

Date Published: 6 December 2002
PDF: 4 pages
Proc. SPIE 4787, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V, (6 December 2002); doi: 10.1117/12.452494
Show Author Affiliations
Binghua Su, Beijing Institute of Technology (China)
Weiqi Jin, Beijing Institute of Technology (China)
LiHong Niu, Beijing Institute of Technology (China)
Guangrong Liu, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 4787:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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