Skip to yearly menu bar Skip to main content


Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint

Haoyue Tang · Tian Xie · Aosong Feng · Hanyu Wang · Chenyang Zhang · Yang Bai

MR1 & MR2 - Number 120
[ ]
Thu 2 May 8 a.m. PDT — 8:30 a.m. PDT


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.

Live content is unavailable. Log in and register to view live content