Saharia Chitwan, Ho Jonathan, Chan William, Salimans Tim, Fleet David J, Norouzi Mohammad
IEEE Trans Pattern Anal Mach Intell. 2023 Apr;45(4):4713-4726. doi: 10.1109/TPAMI.2022.3204461. Epub 2023 Mar 7.
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.
我们提出了SR3，一种通过重复细化实现图像超分辨率的方法。SR3将去噪扩散概率模型（Ho等人，2020年），（Sohl-Dickstein等人，2015年）应用于图像到图像的转换，并通过随机迭代去噪过程执行超分辨率。输出图像以纯高斯噪声初始化，并使用U-Net架构进行迭代细化，该架构在不同噪声水平下的去噪任务上进行训练，并以低分辨率输入图像为条件。SR3在不同放大倍数的超分辨率任务中，在人脸和自然图像上均表现出强大的性能。我们在CelebA-HQ上的标准8倍人脸超分辨率任务上进行了人工评估，SR3在该任务上实现了接近50%的愚弄率，表明其输出具有逼真的照片效果，而GAN基线的愚弄率不超过34%。我们在ImageNet上的4倍超分辨率任务中评估了SR3，在该任务中，SR3在人工评估和基于高分辨率图像训练的ResNet-50分类器的分类准确率方面均优于基线。我们进一步展示了SR3在级联图像生成中的有效性，在类条件256×256 ImageNet生成挑战中，生成模型与超分辨率模型链接以合成具有竞争力的FID分数的高分辨率图像。
