Blind image deblurring via adaptive dynamical system learning
Risheng Liu, Shichao Cheng, Xin Fan, Zhongxuan Luo · 2017
Blind image deblurring is one of the main phases in most media analysis tasks. Many existing works aim to simultaneously estimate the latent image and the blur kernel under a MAP framework. However, it has been demonstrated that such joint estimation strategies may lead to the undesired trivial solution. In this paper, we propose a learnable nonlinear dynamical system to formulate the image propagation so that the blur kernel estimation can be efficiently controlled by both cues and training data. Our analysis also indicates that the proposed dynamical system is feasible on image modeling socialities. Experimental results on different benchmark image sets evaluate the effectiveness of our proposed approach.