Blind motion deblurring with cycle generative adversarial networks

摘要

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.