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Papers › Gated Multiple Feedback Network for Image Super-Resolution

Gated Multiple Feedback Network for Image Super-Resolution

9 Jul 2019arXiv:1907.04253archive 2025-07-28

Qilei Li, Zhen Li, Lu Lu, Gwanggil Jeon, Kai Liu, Xiaomin Yang

The rapid development of deep learning (DL) has driven single image super-resolution (SR) into a new era. However, in most existing DL based image SR networks, the information flows are solely feedforward, and the high-level features cannot be fully explored. In this paper, we propose the gated multiple feedback network (GMFN) for accurate image SR, in which the representation of low-level features are efficiently enriched by rerouting multiple high-level features. We cascade multiple residual dense blocks (RDBs) and recurrently unfolds them across time. The multiple feedback connections between two adjacent time steps in the proposed GMFN exploits multiple high-level features captured under large receptive fields to refine the low-level features lacking enough contextual information. The elaborately designed gated feedback module (GFM) efficiently selects and further enhances useful information from multiple rerouted high-level features, and then refine the low-level features with the enhanced high-level information. Extensive experiments demonstrate the superiority of our proposed GMFN against state-of-the-art SR methods in terms of both quantitative metrics and visual quality. Code is available at https://github.com/liqilei/GMFN.

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liqilei/GMFN officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Image Super-ResolutionSuper-Resolution

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Super-Resolution BSD100 - 4x upscaling GMFN PSNR 27.74 #24 of 71 Archive leaderboard report
Image Super-Resolution BSD100 - 4x upscaling GMFN SSIM 0.7421 #24 of 71 Archive leaderboard report
Image Super-Resolution Manga109 - 4x upscaling GMFN PSNR 31.24 #30 of 50 Archive leaderboard report
Image Super-Resolution Manga109 - 4x upscaling GMFN SSIM 0.9174 #30 of 50 Archive leaderboard report
Image Super-Resolution Set14 - 4x upscaling GMFN PSNR 28.84 #42 of 104 Archive leaderboard report
Image Super-Resolution Set14 - 4x upscaling GMFN SSIM 0.7888 #42 of 104 Archive leaderboard report
Image Super-Resolution Urban100 - 4x upscaling GMFN PSNR 26.69 #29 of 65 Archive leaderboard report
Image Super-Resolution Urban100 - 4x upscaling GMFN SSIM 0.8048 #29 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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