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arXiv · 1804.08181

Large Receptive Field Networks for High-Scale Image Super-Resolution

George Seif·Dimitrios Androutsos
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Abstract

Convolutional Neural Networks have been the backbone of recent rapid progress in Single-Image Super-Resolution. However, existing networks are very deep with many network parameters, thus having a large memory footprint and being challenging to train. We propose Large Receptive Field Networks which strive to directly expand the receptive field of Super-Resolution networks without increasing depth or parameter count. In particular, we use two different methods to expand the network receptive field: 1-D separable kernels and atrous convolutions. We conduct considerable experiments to study the performance of various arrangement schemes of the 1-D separable kernels and atrous convolution in terms of accuracy (PSNR / SSIM), parameter count, and speed, while focusing on the more challenging high upscaling factors. Extensive benchmark evaluations demonstrate the effectiveness of our approach.

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George Seif, Dimitrios Androutsos. 2018-04-22. Large Receptive Field Networks for High-Scale Image Super-Resolution. https://arxiv.org/abs/1804.08181

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