Solving General Noisy Inverse Problem via Posterior Sampling: A Policy
  Gradient Viewpoint
Haoyue Tang Tian Xie Aosong Feng Hanyu Wang Chenyang Zhang Yang Bai
Solving image inverse problems (e.g., super-resolution and inpainting)
requires generating a high fidelity image that matches the given input (the
low-resolution image or the masked image). By using the input image as
guidance, we can leverage a pretrained diffusion generative model to solve a
wide range of image inverse tasks without task specific model fine-tuning. To
precisely estimate the guidance score function of the input image, we propose
Diffusion Policy Gradient (DPG), a tractable computation method by viewing the
intermediate noisy images as policies and the target image as the states
selected by the policy. Experiments show that our method is robust to both
Gaussian and Poisson noise degradation on multiple linear and non-linear
inverse tasks, resulting into a higher image restoration quality on FFHQ,
ImageNet and LSUN datasets.