SR3: Image Super-Resolution via Iterative 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 8 face super-resolution task on CelebA-HQ, comparing with SOTA GAN methods. SR3 achieves a confusion rate close to 50%, suggesting photo-realistic outputs, while GANs do not exceed a confusion rate of 34%.