The Research of Single Image Super-Resolution Based on the Generative Adversary Networks

Abstract

In order to enhance the visual effects of the reconstructed images in the single image super-resolution, and solve the problem of instability of the training phase of super-resolution with generative adversary network (SRGAN), a new super-resolution model is built with more depth and width, and the corresponding super-resolution algorithm is proposed. The network structure of the original SRGAN is modified, so that deeper and wider convolutional networks can be used with high efficiency. Furthermore, the loss function is employed to balance the training of the generative network and the discriminator network, and solve the instability problem in the training phase. The training images for the network are obtained from the database of ImageNet on the web as other researchers. Experimental results show that images reconstructed by the proposed algorithm has better visual effects than those by the original SRGAN. In addition, the objective measures of PSNR and SSIM of the reconstructed images have also been improved.