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Proceedings Paper

The single-image super-resolution method based on the optimization of neural networks
Author(s): Chunjiang Duanmu; Yi Lei
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Paper Abstract

In the conventional single-image super-resolution algorithms, they assume that the sparse coefficients of the low-resolution patches and the corresponding high-resolution patches are the same. However, from our research, it is found that these coefficients are different most of the times. In this paper, the mapping relationship between the low-resolution coefficients and the high-resolution coefficients are learned based on neural networks. In this method, the low-resolution and high-resolution coefficients are first obtained from training images. Then, they are the inputs for a neural network to train this network. Finally, they are used in the reconstruction of the high-resolution image patches. Experimental results show that the proposed method has better performance than the original state-of-the-art algorithms.

Paper Details

Date Published: 31 January 2020
PDF: 6 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114270N (31 January 2020); doi: 10.1117/12.2550456
Show Author Affiliations
Chunjiang Duanmu, Zhejiang Normal Univ. (China)
Yi Lei, Zhejiang Normal Univ. (China)


Published in SPIE Proceedings Vol. 11427:
Second Target Recognition and Artificial Intelligence Summit Forum
Tianran Wang; Tianyou Chai; Huitao Fan; Qifeng Yu, Editor(s)

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