Generative Adversarial Networks with Enhanced Symmetric Residual Units for Single Image Super-Resolution

Abstract

In this paper, we propose a new generative adversarial network (GAN) with enhanced symmetric residual units for single image super-resolution (ERGAN). ERGAN consists of a generator network and a discriminator network. The former can maximally reconstruct a super-resolution image similar to the original image. This lead to the discriminator network cannot distinguish the image from the training data or the generated sample. Combining residual units used in the generator network, ERGAN can retain the high-frequency features and alleviate the difficulty training in deep networks. Moreover, we constructed the symmetric skip-connections in residual units. This reused features generated from the low-level, and learned more high-frequency content. Moreover, ERGAN reconstructed the super-resolution image by four times the length and width of the original image and exhibited better visual characteristics. Experimental results on extensive benchmark evaluation showed that ERGAN significantly outperformed state-of-the-art approaches in terms of accuracy and vision.