The Google research team presented SR3, an approach to image Super-Resolution that is based on Repeated Refinement. SR3 uses denoising diffusion probabilistic models to conditional image generation and performs super-resolution with a stochastic denoising process.
The team noted that "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."
