Papers › Image Super-Resolution Using Very Deep Residual Channel Attention Networks
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
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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Code
Syntology Ran 10 of 22 code samples harvested from 5 repositories linked to this paper; 12 have no recorded run. Of those that ran: 1 ran · our draft was wrong; 9 ran with no contract checked.
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20 repositories listed; official and paper-mentioned ones first.
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Code Syntology ran Syntology
22 samples harvested; 10 ran; 0 honoured the contract we drafted; 12 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 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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