面向动态场景去模糊的对偶学习生成对抗网络

For the problem of dynamic scene deblurring, a dual learning generative adversarial network (DLGAN) is proposed in this paper. The network can use unpaired blurry and sharp images to perform image deblurring calculations in the training mode of dual learning, which no longer requires the training image set to be a pair of blurry and their corresponding sharp images. The DLGAN uses the duality between the deblurring task and the reblurring task to establish a feedback signal, and uses this signal to constrain the deblurring task and the reblurring task to learn and update each other from two different directions until convergence. Experimental results show that the DLGAN has a better performance compared to nine image deblurring methods trained with paired datasets in structural similarity and visualization evaluation.