Blind motion deblurring with cycle generative adversarial networks

Blind motion deblurring is one of the most basic and challenging problems in image processing and computer vision. It aims to recover a sharp image from its blurred version knowing nothing about the blurring process. Many existing methods use the maximum a posteriori or expectation maximization framework to tackle this problem, but they cannot handle well the natural images with high-frequency features. Most recently, deep neural networks have been emerging as a powerful tool for image deblurring. In this paper, we show that encoder–decoder architecture gives better results for image deblurring tasks. In addition, we propose a novel end-to-end learning model that refines the generative adversarial network by many novel strategies to tackle the problem of image deblurring. Experimental results show that our model can capture high-frequency features well, and achieve the competitive performance.