SOTAVerified

Deeply-Recursive Convolutional Network for Image Super-Resolution

2015-11-14CVPR 2016Code Available0· sign in to hype

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.

Tasks

Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
BSD100 - 2x upscalingDRCN [[Kim et al.2016b]]PSNR31.85Unverified
BSD100 - 4x upscalingDRCNPSNR27.21Unverified
Set14 - 2x upscalingDRCN [[Kim et al.2016b]]PSNR33.04Unverified
Set14 - 4x upscalingDRCNPSNR28.02Unverified
Set5 - 2x upscalingDRCN [[Kim et al.2016b]]PSNR37.63Unverified
Urban100 - 2x upscalingDRCN [[Kim et al.2016b]]PSNR30.75Unverified

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