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A progressive approach for single image super-resolution
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Paper Abstract

Convolutional neural network has achieved excellent success in single image super-resolution. In this paper, we present a progressive approach which reconstructs a high resolution image and optimizes the network at each level. In addition, our method can generate multi-scale HR image by one feed-forward network. The proposed method also utilizes the relationships among different scales, which help our network perform well on large scaling factors. Experiments on benchmark dataset demonstrate that our method achieves competitive performance against most state-of-the-art methods, especially for large scaling factors (e.g. 8×).

Paper Details

Date Published: 31 July 2019
PDF: 5 pages
Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 1119805 (31 July 2019); doi: 10.1117/12.2540564
Show Author Affiliations
Yongbo Liang, Nanjing Univ. of Science and Technology (China)
Guo Cao, Nanjing Univ. of Science and Technology (China)
Xuesong Li, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 11198:
Fourth International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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