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|a 10.1109/TPAMI.2022.3204461
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|a eng
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|a Saharia, Chitwan
|e verfasserin
|4 aut
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|a Image Super-Resolution via Iterative Refinement
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|c 2023
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|a Text
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|a ƒaComputermedien
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|a Date Completed 10.04.2023
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|a Date Revised 10.04.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3 adapts denoising diffusion probabilistic models (Ho et al. 2020), (Sohl-Dickstein et al. 2015) to image-to-image translation, and performs super-resolution through a stochastic iterative denoising process. Output images are initialized with pure Gaussian noise and iteratively refined using a U-Net architecture that is trained on denoising at various noise levels, conditioned on a low-resolution input image. SR3 exhibits strong performance on super-resolution tasks at different magnification factors, on faces and natural images. We conduct human evaluation on a standard 8× face super-resolution task on CelebA-HQ for which SR3 achieves a fool rate close to 50%, suggesting photo-realistic outputs, while GAN baselines do not exceed a fool rate of 34%. We evaluate SR3 on a 4× super-resolution task on ImageNet, where SR3 outperforms baselines in human evaluation and classification accuracy of a ResNet-50 classifier trained on high-resolution images. We further show the effectiveness of SR3 in cascaded image generation, where a generative model is chained with super-resolution models to synthesize high-resolution images with competitive FID scores on the class-conditional 256×256 ImageNet generation challenge
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|a Journal Article
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1 |
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|a Ho, Jonathan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chan, William
|e verfasserin
|4 aut
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| 700 |
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|a Salimans, Tim
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Fleet, David J
|e verfasserin
|4 aut
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|a Norouzi, Mohammad
|e verfasserin
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 4 vom: 12. Apr., Seite 4713-4726
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|x 1939-3539
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|g volume:45
|g year:2023
|g number:4
|g day:12
|g month:04
|g pages:4713-4726
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|u http://dx.doi.org/10.1109/TPAMI.2022.3204461
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