Conditional Velocity Score Estimation for Image Restoration

Ziqiang Shi, Rujie Liu; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 179-188

This paper proposes a new image restoration method by introducing a velocity variable on top of the data position during recovery. Under the guidance of the degraded image, it can effectively and dynamically control the direction of the diffusion path in the reverse-time stochastic differential equation (SDE). So the crucial factor is how to combine the degraded signal as a guide in this second-order reverse process with velocity, especially in the moving direction as a diffusion path. To this end, we propose a conditional velocity score approximation (CVSA) method based on the Bayesian principle to approximate the true posterior conditional velocity score by the sum of a priori conditional velocity score and an observation velocity score of the degraded measurement at the current moment. Our method is versatile from two perspectives. It can be used for both non-blind restoration and blind restoration. At the same time, there is almost no requirement for the degradation operator, and both linear and nonlinear tasks are acceptable. In non-blind restoration, including deblurring, inpainting, super-resolution, phase retrieval, and blind restoration, such as deblurring experiments, CVSA is better than other methods and achieves a new state-of-the-art.