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arXiv · 1511.04587

Accurate Image Super-Resolution Using Very Deep Convolutional Networks

Jiwon Kim·Jung Kwon Lee·Kyoung Mu Lee
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Abstract

We present a highly accurate single-image super-resolution (SR) method. Our method uses a very deep convolutional network inspired by VGG-net used for ImageNet classification \cite{simonyan2015very}. We find increasing our network depth shows a significant improvement in accuracy. Our final model uses 20 weight layers. By cascading small filters many times in a deep network structure, contextual information over large image regions is exploited in an efficient way. With very deep networks, however, convergence speed becomes a critical issue during training. We propose a simple yet effective training procedure. We learn residuals only and use extremely high learning rates ($10^4$ times higher than SRCNN \cite{dong2015image}) enabled by adjustable gradient clipping. Our proposed method performs better than existing methods in accuracy and visual improvements in our results are easily noticeable.

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BibTeXRIS

Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee. 2016-11-11. Accurate Image Super-Resolution Using Very Deep Convolutional Networks. https://arxiv.org/abs/1511.04587

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