Papers › Image Super-Resolution via Iterative Refinement

Image Super-Resolution via Iterative Refinement

15 Apr 2021arXiv:2104.07636archive 2025-07-28

Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J. Fleet, Mohammad Norouzi

We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3 adapts denoising diffusion probabilistic models to conditional image generation and performs super-resolution through a stochastic denoising process. Inference starts with pure Gaussian noise and iteratively refines the noisy output using a U-Net model trained on denoising at various noise levels. SR3 exhibits strong performance on super-resolution tasks at different magnification factors, on faces and natural images. We conduct human evaluation on a standard 8X face super-resolution task on CelebA-HQ, comparing with SOTA GAN methods. SR3 achieves a fool rate close to 50%, suggesting photo-realistic outputs, while GANs do not exceed a fool rate of 34%. We further show the effectiveness of SR3 in cascaded image generation, where generative models are chained with super-resolution models, yielding a competitive FID score of 11.3 on ImageNet.

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GaussianDiffusion lshaw8317/mlmcfordms/Superresolution/model/sr3_modules/diffusion.py community (archive-listed) ran · metamorphic tier: deterministic no licence file found · pointer only · 66ca813ef88512ea · report
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GaussianDiffusion Janspiry/Image-Super-Resolution-via-Iterative-Refinement/model/sr3_modules/diffusion.py community (archive-listed) unverified Apache-2.0 (permissive) · 1a97dc5a5793d7e7 · report

Tasks

Conditional Image GenerationDenoisingImage GenerationImage Super-ResolutionSuper-Resolution

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Super-Resolution CelebA-HQ 128x128 SR3 Consistency 2.68 #4 of 4 Archive leaderboard report
Image Super-Resolution CelebA-HQ 128x128 SR3 PSNR 23.04 #4 of 4 Archive leaderboard report
Image Super-Resolution CelebA-HQ 128x128 SR3 SSIM 0.65 #4 of 4 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

Concatenated Skip ConnectionConvolutionDiffusionMax PoolingReLUU-Net

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