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arXiv · 1511.04491

Deeply-Recursive Convolutional Network for Image Super-Resolution

Jiwon Kim·Jung Kwon Lee·Kyoung Mu Lee
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Abstract

We propose an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN). Our network has a very deep recursive layer (up to 16 recursions). Increasing recursion depth can improve performance without introducing new parameters for additional convolutions. Albeit advantages, learning a DRCN is very hard with a standard gradient descent method due to exploding/vanishing gradients. To ease the difficulty of training, we propose two extensions: recursive-supervision and skip-connection. Our method outperforms previous methods by a large margin.

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Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee. 2015-11-14. Deeply-Recursive Convolutional Network for Image Super-Resolution. https://arxiv.org/abs/1511.04491

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