Papers › Accurate Image Super-Resolution Using Very Deep Convolutional Networks

Accurate Image Super-Resolution Using Very Deep Convolutional Networks

14 Nov 2015CVPR 2016 6arXiv:1511.04587archive 2025-07-28

Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee

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 (10⁴ 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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Lornatang/DRRN-PyTorch mentioned on GitHubpytorchApache-2.0 report
Lornatang/VDSR-PyTorch mentioned on GitHubpytorchApache-2.0 report
Nhat-Thanh/VDSR-Pytorch mentioned on GitHubpytorchMIT report
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cubic Lornatang/DRRN-PyTorch/imgproc.py community (archive-listed) unverified Apache-2.0 (permissive) · 0d462506dbb0db2d · report
image2tensor Lornatang/DRRN-PyTorch/imgproc.py community (archive-listed) unverified Apache-2.0 (permissive) · 4579f4a0e343f30e · report
read_image Nhat-Thanh/VDSR-Pytorch/utils/common.py community (archive-listed) unverified MIT (permissive) · 4d448d32e15f04c9 · report
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ycbcr2rgb Nhat-Thanh/VDSR-Pytorch/utils/common.py community (archive-listed) unverified MIT (permissive) · ef42ea280e97a9f8 · report

Tasks

General ClassificationImage Super-ResolutionSuper-ResolutionVideo Super-Resolution

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Super-Resolution IXI VDSR PSNR 2x T2w 38.65 #7 of 9 Archive leaderboard report
Image Super-Resolution IXI VDSR PSNR 4x T2w 30.79 #7 of 9 Archive leaderboard report
Image Super-Resolution IXI VDSR SSIM 4x T2w 0.9240 #7 of 9 Archive leaderboard report
Image Super-Resolution IXI VDSR SSIM for 2x T2w 0.9836 #7 of 9 Archive leaderboard report
Image Super-Resolution Manga109 - 4x upscaling VDSR PSNR 28.83 #43 of 50 Archive leaderboard report
Image Super-Resolution Manga109 - 4x upscaling VDSR SSIM 0.8870 #43 of 50 Archive leaderboard report
Image Super-Resolution Set14 - 2x upscaling VDSR [[Kim et al.2016a]] PSNR 33.03 #32 of 35 Archive leaderboard report
Image Super-Resolution Set5 - 2x upscaling VDSR [[Kim et al.2016a]] PSNR 37.53 #36 of 41 Archive leaderboard report
Image Super-Resolution Urban100 - 2x upscaling VDSR [[Kim et al.2016a]] PSNR 30.76 #27 of 29 Archive leaderboard report
Image Super-Resolution VggFace2 - 8x upscaling VDSR PSNR 22.50 #4 of 7 Archive leaderboard report
Image Super-Resolution WebFace - 8x upscaling VDSR PSNR 23.65 #4 of 7 Archive leaderboard report
Video Super-Resolution MSU Video Upscalers: Quality Enhancement VDSR PSNR 25.89 #35 of 48 Archive leaderboard report
Video Super-Resolution MSU Video Upscalers: Quality Enhancement VDSR SSIM 0.917 #35 of 48 Archive leaderboard report
Video Super-Resolution MSU Video Upscalers: Quality Enhancement VDSR VMAF 36.46 #35 of 48 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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