The single-image super-resolution method based on the optimization of neural networks

Chunjiang Duanmu; Yi Lei

doi:10.1117/12.2550456

31 January 2020 The single-image super-resolution method based on the optimization of neural networks

Chunjiang Duanmu, Yi Lei

Proceedings Volume 11427, Second Target Recognition and Artificial Intelligence Summit Forum; 114270N (2020) https://doi.org/10.1117/12.2550456
Event: Second Target Recognition and Artificial Intelligence Summit Forum, 2019, Changchun, China

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.

Citation Download Citation

Chunjiang Duanmu and Yi Lei "The single-image super-resolution method based on the optimization of neural networks", Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114270N (31 January 2020); https://doi.org/10.1117/12.2550456

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