Papers › Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation
Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation
Qinyan Dai, Juncheng Li, Qiaosi Yi, Faming Fang, Guixu Zhang
Under stereo settings, the problem of image super-resolution (SR) and disparity estimation are interrelated that the result of each problem could help to solve the other. The effective exploitation of correspondence between different views facilitates the SR performance, while the high-resolution (HR) features with richer details benefit the correspondence estimation. According to this motivation, we propose a Stereo Super-Resolution and Disparity Estimation Feedback Network (SSRDE-FNet), which simultaneously handles the stereo image super-resolution and disparity estimation in a unified framework and interact them with each other to further improve their performance. Specifically, the SSRDE-FNet is composed of two dual recursive sub-networks for left and right views. Besides the cross-view information exploitation in the low-resolution (LR) space, HR representations produced by the SR process are utilized to perform HR disparity estimation with higher accuracy, through which the HR features can be aggregated to generate a finer SR result. Afterward, the proposed HR Disparity Information Feedback (HRDIF) mechanism delivers information carried by HR disparity back to previous layers to further refine the SR image reconstruction. Extensive experiments demonstrate the effectiveness and advancement of SSRDE-FNet.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Stereo Image Super-Resolution | Flickr1024 - 2x upscaling | SSRDE-FNet | PSNR | 28.85 | #7 of 10 | Archive leaderboard | report |
| Stereo Image Super-Resolution | Flickr1024 - 4x upscaling | SSRDE-FNet | PSNR | 23.59 | #5 of 8 | Archive leaderboard | report |
| Stereo Image Super-Resolution | KITTI2012 - 2x upscaling | SSRDE-FNet | PSNR | 31.23 | #2 of 5 | Archive leaderboard | report |
| Stereo Image Super-Resolution | KITTI2012 - 4x upscaling | SSRDE-FNet | PSNR | 26.70 | #6 of 9 | Archive leaderboard | report |
| Stereo Image Super-Resolution | KITTI2015 - 2x upscaling | SSRDE-FNet | PSNR | 30.90 | #6 of 9 | Archive leaderboard | report |
| Stereo Image Super-Resolution | KITTI2015 - 4x upscaling | SSRDE-FNet | PSNR | 26.43 | #6 of 9 | Archive leaderboard | report |
| Stereo Image Super-Resolution | Middlebury - 2x upscaling | SSRDE-FNet | PSNR | 35.09 | #6 of 9 | Archive leaderboard | report |
| Stereo Image Super-Resolution | Middlebury - 4x upscaling | SSRDE-FNet | PSNR | 29.38 | #6 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.
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