Adaptive Image Deblurring via Tanner Graph Representation and Belief Propagation
In this paper, we propose a deblurring framework based on a factor graph representation of the image and the image formation process. Each pixel is described by a variable node, while the statistical relation among pixels is formulated by two sets of check nodes, describing the local image structures and the image formation process, respectively. Belief propagation is employed to solve the pixel values and it is reduced to mechanisms to generate and fuse predictions for each pixel iteratively. A key work is that we analyzed the origin of ringing artifacts and found that it is due to the propagation of estimation error in previous iterations. We propose a method to estimate the uncertainty in each pixel of previous estimation, which is then used to adapt the generation and fusion of prediction in the next iteration. Experimental results show that the proposed solution can significantly eliminate ringing artifacts without employing any image priors.