Image Deblurring Method Based on a Dual-Discriminator Weighted Generative Adversarial Network

The original generative adversarial network (GAN) is susceptible to the problems of vanishing gradients and mode collapse during the training process, and its deblurring effectiveness is poor. This study proposes an image deblurring method using a dual-discriminator weighted GAN. To extend the original GAN, a discriminator network is added to combine the forward and reverse Kullback–Leibler (KL) divergences into an objective function, and weights are used to adjust the ratio of forward and reverse KL divergences to leverage the complementary characteristics of the two divergences to avoid the formation of undesirable patterns in the process of learning clear pictures. Theoretical analysis proves that when an optimal discriminator is given, the difference between the forward and reverse KL divergences between real and generated data can be minimized. Experimental results demonstrate that compared to the existing methods, the proposed method can restore the details of an image more realistically and provides better performance in terms of the evaluation indexes of peak signal-to-noise ratio and structural similarity.