Papers › NAFSSR: Stereo Image Super-Resolution Using NAFNet

NAFSSR: Stereo Image Super-Resolution Using NAFNet

19 Apr 2022arXiv:2204.08714archive 2025-07-28

Xiaojie Chu, Liangyu Chen, Wenqing Yu

Stereo image super-resolution aims at enhancing the quality of super-resolution results by utilizing the complementary information provided by binocular systems. To obtain reasonable performance, most methods focus on finely designing modules, loss functions, and etc. to exploit information from another viewpoint. This has the side effect of increasing system complexity, making it difficult for researchers to evaluate new ideas and compare methods. This paper inherits a strong and simple image restoration model, NAFNet, for single-view feature extraction and extends it by adding cross attention modules to fuse features between views to adapt to binocular scenarios. The proposed baseline for stereo image super-resolution is noted as NAFSSR. Furthermore, training/testing strategies are proposed to fully exploit the performance of NAFSSR. Extensive experiments demonstrate the effectiveness of our method. In particular, NAFSSR outperforms the state-of-the-art methods on the KITTI 2012, KITTI 2015, Middlebury, and Flickr1024 datasets. With NAFSSR, we won 1st place in the NTIRE 2022 Stereo Image Super-resolution Challenge. Codes and models will be released at https://github.com/megvii-research/NAFNet.

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Code

megvii-research/NAFNet officialmentioned in papermentioned on GitHubpytorch report
megvii-research/TLC mentioned on GitHubpytorchNOASSERTION report
megvii-research/tlsc mentioned on GitHubpytorchNOASSERTION report
setsunil/dsdnet mentioned on GitHubpytorchNOASSERTION report

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Tasks

Image RestorationImage Super-ResolutionStereo Image Super-ResolutionSuper-Resolution

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Stereo Image Super-Resolution Flickr1024 - 2x upscaling NAFSSR-L PSNR 29.68 #3 of 10 Archive leaderboard report
Stereo Image Super-Resolution Flickr1024 - 2x upscaling NAFSSR-B PSNR 29.54 #4 of 10 Archive leaderboard report
Stereo Image Super-Resolution Flickr1024 - 4x upscaling NAFSSR-L PSNR 24.17 #2 of 8 Archive leaderboard report
Stereo Image Super-Resolution Flickr1024 - 4x upscaling NAFSSR-B PSNR 24.07 #3 of 8 Archive leaderboard report
Stereo Image Super-Resolution KITTI2012 - 2x upscaling NAFSSR-L PSNR 31.60 #3 of 5 Archive leaderboard report
Stereo Image Super-Resolution KITTI2012 - 2x upscaling NAFSSR-B PSNR 31.55 #4 of 5 Archive leaderboard report
Stereo Image Super-Resolution KITTI2012 - 4x upscaling NAFSSR-L PSNR 27.12 #2 of 9 Archive leaderboard report
Stereo Image Super-Resolution KITTI2012 - 4x upscaling NAFSSR-B PSNR 27.08 #3 of 9 Archive leaderboard report
Stereo Image Super-Resolution KITTI2015 - 2x upscaling NAFSSR-L PSNR 31.25 #2 of 9 Archive leaderboard report
Stereo Image Super-Resolution KITTI2015 - 2x upscaling NAFSSR-B PSNR 31.22 #3 of 9 Archive leaderboard report
Stereo Image Super-Resolution KITTI2015 - 4x upscaling NAFSSR-L PSNR 26.96 #1 of 9 Archive leaderboard report
Stereo Image Super-Resolution KITTI2015 - 4x upscaling NAFSSR-B PSNR 26.91 #2 of 9 Archive leaderboard report
Stereo Image Super-Resolution Middlebury - 2x upscaling NAFSSR-L PSNR 35.88 #2 of 9 Archive leaderboard report
Stereo Image Super-Resolution Middlebury - 2x upscaling NAFSSR-B PSNR 35.68 #4 of 9 Archive leaderboard report
Stereo Image Super-Resolution Middlebury - 4x upscaling NAFSSR-L PSNR 30.20 #2 of 9 Archive leaderboard report
Stereo Image Super-Resolution Middlebury - 4x upscaling NAFSSR-B PSNR 30.04 #3 of 9 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.

Methods

NAFNet

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