Papers › Image Super-Resolution Using Very Deep Residual Channel Attention Networks

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

8 Jul 2018ECCV 2018 9arXiv:1807.02758archive 2025-07-28

Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, Yun Fu

Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train. The low-resolution inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hindering the representational ability of CNNs. To solve these problems, we propose the very deep residual channel attention networks (RCAN). Specifically, we propose a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections. Each residual group contains some residual blocks with short skip connections. Meanwhile, RIR allows abundant low-frequency information to be bypassed through multiple skip connections, making the main network focus on learning high-frequency information. Furthermore, we propose a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels. Extensive experiments show that our RCAN achieves better accuracy and visual improvements against state-of-the-art methods.

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20 repositories listed; official and paper-mentioned ones first.

yulunzhang/RCAN officialmentioned in papermentioned on GitHubpytorch report
AD2605/Single-Image-Super-Resolution mentioned on GitHubpytorch report
ChaofWang/AWSRN mentioned on GitHubpytorch report
Feynman1999/MgeEditing mentioned on GitHub report
Lornatang/CSNLN-PyTorch mentioned on GitHubpytorchApache-2.0 report
coloquinte/torchsr mentioned on GitHubpytorch report
dongheehand/RCAN-tf mentioned on GitHubtf report
fengye-lu/RCAN-pytorch mentioned on GitHubpytorch report
kailigo/pacl mentioned on GitHubpytorch report
kongdebug/RCAN-Paddle mentioned on GitHubpaddle report
saeed-anwar/DRLN mentioned on GitHubpytorchMIT report
shesay-noway/Super-Resolution-RCAN mentioned on GitHubpytorch report
wdd233/RCAN mentioned on GitHubpytorch report
zhusiling/RCAN mentioned on GitHubpytorch report

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AWMS ChaofWang/AWSRN/code/src/model/awsrn.py community (archive-listed) ran fingerprinted MIT (permissive) · d19d0c8792f5b99a · report
AWRU ChaofWang/AWSRN/code/src/model/awsrn.py community (archive-listed) ran fingerprinted MIT (permissive) · 2ec67cec615644ea · report
LFB ChaofWang/AWSRN/code/src/model/awsrn.py community (archive-listed) ran fingerprinted MIT (permissive) · 1d26ee244b0e0aa3 · report
MODEL ChaofWang/AWSRN/code/src/model/awsrn.py community (archive-listed) ran fingerprinted MIT (permissive) · c81b412e3e5ff687 · report
MeanShift coloquinte/torchsr/torchsr/models/rcan.py community (archive-listed) ran · metamorphic tier: invariant fingerprinted MIT (permissive) · 8affcfa340387a49 · report
ResidualGroup coloquinte/torchsr/torchsr/models/rcan.py community (archive-listed) ran MIT (permissive) · 464cf568ffdadee9 · report
Scale ChaofWang/AWSRN/code/src/model/awsrn.py community (archive-listed) ran fingerprinted MIT (permissive) · 1feb1832e4fe7f2e · report
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RCAB coloquinte/torchsr/torchsr/models/rcan.py community (archive-listed) unverified MIT (permissive) · c618cce8b87eb8fe · report
RCAN coloquinte/torchsr/torchsr/models/rcan.py community (archive-listed) unverified MIT (permissive) · 7c4c09142bd8b3be · report
RCA_net dongheehand/RCAN-tf/RCA_net.py community (archive-listed) unverified no licence file found · pointer only · 916710c01e731111 · report
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reduce_sum Lornatang/CSNLN-PyTorch/model.py community (archive-listed) unverified Apache-2.0 (permissive) · f9555708bbf55bd4 · report
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tensor2image Lornatang/CSNLN-PyTorch/imgproc.py community (archive-listed) unverified Apache-2.0 (permissive) · dd73a13fd75b72d0 · report

Tasks

Image Super-ResolutionSuper-Resolution

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Super-Resolution BSD100 - 4x upscaling RCAN PSNR 27.77 #21 of 71 Archive leaderboard report
Image Super-Resolution BSD100 - 4x upscaling RCAN SSIM 0.7436 #21 of 71 Archive leaderboard report
Image Super-Resolution Manga109 - 4x upscaling RCAN PSNR 31.22 #31 of 50 Archive leaderboard report
Image Super-Resolution Manga109 - 4x upscaling RCAN SSIM 0.9173 #31 of 50 Archive leaderboard report
Image Super-Resolution Set14 - 4x upscaling RCAN PSNR 28.87 #39 of 104 Archive leaderboard report
Image Super-Resolution Set14 - 4x upscaling RCAN SSIM 0.7889 #39 of 104 Archive leaderboard report
Image Super-Resolution Urban100 - 4x upscaling RCAN PSNR 26.82 #27 of 65 Archive leaderboard report
Image Super-Resolution Urban100 - 4x upscaling RCAN SSIM 0.8087 #27 of 65 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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