MLP neural network super-resolution restoration for the undersampled low-resolution image

Binghua Su; Weiqi Jin; LiHong Niu; Guangrong Liu

doi:10.1117/12.452494

6 December 2002 MLP neural network super-resolution restoration for the undersampled low-resolution image

Binghua Su, Weiqi Jin, LiHong Niu, Guangrong Liu

Proceedings Volume 4787, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V; (2002) https://doi.org/10.1117/12.452494
Event: International Symposium on Optical Science and Technology, 2002, Seattle, WA, United States

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.

Citation Download Citation

Binghua Su, Weiqi Jin, LiHong Niu, and Guangrong Liu "MLP neural network super-resolution restoration for the undersampled low-resolution image", Proc. SPIE 4787, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V, (6 December 2002); https://doi.org/10.1117/12.452494

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