Image Super-Resolution via Iterative Refinement

• URL: http://arxiv.org/abs/2104.07636v1
• Date: Thu, 15 Apr 2021 17:50:42 GMT
• Title: Image Super-Resolution via Iterative Refinement
• Authors: Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J. Fleet, Mohammad Norouzi
• Abstract summary: SR3 is an approach to image Super-Resolution via Repeated Refinement. It adapts probabilistic denoising diffusion models to conditional image generation. It exhibits strong performance on super-resolution tasks at different magnification factors.
• Score: 53.57766722279425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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