JPEG Artifact Correction using Denoising Diffusion Restoration Models

Understand

Diffusion models can be used as learned priors for solving various inverse problems.

• However, most existing approaches are restricted to linear inverse problems, limiting their applicability to more general cases.
• In this paper, we build upon Denoising Diffusion Restoration Models (DDRM) and propose a method for solving some non-linear inverse problems.
• We leverage the pseudo-inverse operator used in DDRM and generalize this concept for other measurement operators, which allows us to use pre-trained unconditional diffusion models for applications such as JPEG artifact correction.