Image Super-Resolution via Progressive Cascading Residual Network

Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 791-799

Abstract


The problem of enhancing the resolution of a single low-resolution image has been popularly addressed by recent deep learning techniques. However, many deep learning approaches still fail to deal with extreme super-resolution scenarios because of the instability of training. In this paper, we address this issue by adapting a progressive learning scheme to the deep convolutional neural network. In detail, the overall training proceeds in multiple stages so that the model gradually increases the output image resolution. In our experiments, we show that this property yields a large performance gain compared to the non-progressive learning methods.

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[bibtex]
@InProceedings{Ahn_2018_CVPR_Workshops,
author = {Ahn, Namhyuk and Kang, Byungkon and Sohn, Kyung-Ah},
title = {Image Super-Resolution via Progressive Cascading Residual Network},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}