Deeply-Recursive Convolutional Network for Image Super-Resolution

CVPR 2016  ·  Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee ·

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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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Image Super-Resolution BSD100 - 2x upscaling DRCN [[Kim et al.2016b]] PSNR 31.85 # 22
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Image Super-Resolution BSD100 - 4x upscaling DRCN PSNR 27.21 # 43
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SSIM 0.7493 # 10
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MOS 2.12 # 3
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Image Super-Resolution Set14 - 2x upscaling DRCN [[Kim et al.2016b]] PSNR 33.04 # 21
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Image Super-Resolution Set14 - 4x upscaling DRCN PSNR 28.02 # 61
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SSIM 0.8074 # 3
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MOS 2.84 # 3
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Image Super-Resolution Set5 - 2x upscaling DRCN [[Kim et al.2016b]] PSNR 37.63 # 23
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Image Super-Resolution Urban100 - 2x upscaling DRCN [[Kim et al.2016b]] PSNR 30.75 # 20
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