A Diffusion Model with State Estimation for Degradation-Blind Inverse Imaging

Liya Ji, Zhefan Rao, Sinno Jialin Pan, Chenyang Lei, Qifeng Chen

Abstract:

Solving the task of inverse imaging problems can restore unknown clean images from input measurements that have incomplete information.
Utilizing powerful generative models, such as denoising diffusion models, could better tackle the ill-posed issues of inverse problems with the distribution prior of the unknown clean images.
We propose a learnable state-estimator-based diffusion model to incorporate the measurements into the reconstruction process.
Our method makes efficient use of the pre-trained diffusion models with computational feasibility compared to the conditional diffusion models, which need to be trained from scratch. In addition, our pipeline does not require explicit knowledge of the image degradation operator or make the assumption of its form, unlike many other works that use the pre-trained diffusion models at the test time.
The experiments on three typical inverse imaging problems (both linear and non-linear), inpainting, deblurring, and JPEG compression restoration, have comparable results with the state-of-the-art methods.