Papers › SRFlow: Learning the Super-Resolution Space with Normalizing Flow

SRFlow: Learning the Super-Resolution Space with Normalizing Flow

25 Jun 2020ECCV 2020 8arXiv:2006.14200archive 2025-07-28

Andreas Lugmayr, Martin Danelljan, Luc van Gool, Radu Timofte

Super-resolution is an ill-posed problem, since it allows for multiple predictions for a given low-resolution image. This fundamental fact is largely ignored by state-of-the-art deep learning based approaches. These methods instead train a deterministic mapping using combinations of reconstruction and adversarial losses. In this work, we therefore propose SRFlow: a normalizing flow based super-resolution method capable of learning the conditional distribution of the output given the low-resolution input. Our model is trained in a principled manner using a single loss, namely the negative log-likelihood. SRFlow therefore directly accounts for the ill-posed nature of the problem, and learns to predict diverse photo-realistic high-resolution images. Moreover, we utilize the strong image posterior learned by SRFlow to design flexible image manipulation techniques, capable of enhancing super-resolved images by, e.g., transferring content from other images. We perform extensive experiments on faces, as well as on super-resolution in general. SRFlow outperforms state-of-the-art GAN-based approaches in terms of both PSNR and perceptual quality metrics, while allowing for diversity through the exploration of the space of super-resolved solutions.

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andreas128/SRFlow officialmentioned in paperpytorchNOASSERTION report
Zhangyanbo/iResNetLab mentioned on GitHubpytorch report
andreas128/NTIRE21_Learning_SR_Space mentioned on GitHubpytorch report
friedmanroy/hi-generation mentioned on GitHubpytorchMIT report
liyuantsao/BFSR mentioned on GitHubpytorch report
liyuantsao/flowsr-lp mentioned on GitHubpytorch report
seungho-snu/fxsr mentioned on GitHubpytorch report

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2ran · our draft was wrong
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Tasks

DiversityImage ManipulationImage Super-ResolutionSuper-Resolution

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Super-Resolution DIV2K val - 4x upscaling SRFlow LPIPS 0.12 #8 of 21 Archive leaderboard report
Image Super-Resolution DIV2K val - 4x upscaling SRFlow LRPSNR 49.96 #8 of 21 Archive leaderboard report
Image Super-Resolution DIV2K val - 4x upscaling SRFlow NIQE 3.57 #8 of 21 Archive leaderboard report
Image Super-Resolution DIV2K val - 4x upscaling SRFlow PSNR 27.09 #8 of 21 Archive leaderboard report
Image Super-Resolution DIV2K val - 4x upscaling SRFlow SSIM 0.76 #8 of 21 Archive leaderboard report

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