Deep Priors inside an Unrolled and Adaptive Deconvolution Model

Hung-Chih Ko, Je-Yuan Chang, Jian-Jiun Ding; Proceedings of the Asian Conference on Computer Vision (ACCV), 2020

Image deconvolution is an essential but ill-posed problem even if the degradation kernel is known. Recently, learning based methods have demonstrated superior image restoration quality in comparison to traditional methods which are typically based on empirical statistics and parameter adjustment. Though coming up with outstanding performance, most of the plug-and-play priors are trained in a specific degradation model, leading to inferior performance on restoring high-frequency components. To address this problem, a deblurring architecture that adopts (1) adaptive deconvolution modules and (2) learning based image prior solvers is proposed. The adaptive deconvolution module adjusts the regularization weight locally to well process both smooth and non-smooth regions. Moreover, a cascade made of image priors is learned from the mapping between intermediates thus robust to arbitrary noise, aliasing, and artifact. According to our analysis, the proposed architecture can achieve a significant improvement on the convergence rate and result in an even better restoration performance.