Taming Diffusion Prior for Image Super-Resolution with Domain Shift SDEs

Understand

Diffusion-based image super-resolution (SR) models have attracted substantial interest due to their powerful image restoration capabilities.

• However, prevailing diffusion models often struggle to strike an optimal balance between efficiency and performance.
• Typically, they either neglect to exploit the potential of existing extensive pretrained models, limiting their generative capacity, or they necessitate a dozens of forward passes starting from random noises, compromising inference efficiency.
• In this paper, we present DoSSR, a Domain Shift diffusion-based SR model that capitalizes on the generative powers of pretrained diffusion models while significantly enhancing efficiency by initiating the diffusion process with low-resolution (LR) images.