关键词: 超分辨率, 生成对抗网络, 自注意力增强, 深度残差网络, 多样性

Abstract:

Despite the deep convolutional neural networks make breakthroughs in accuracy and speed of single image super-resolution, the central problems remain largely unsolved: the reconstruction results for traditional methods which are not obvious and too smooth. In this paper, a single image super resolution method based on coupled generative adversarial networks is proposed to solve the central problems. The deep residual network and the deep convolutional neural network are used as the generator and the discriminator. The attention augmented convolution is applied to the generator. In order to enhance the quality of the generated image and the stability of the training process, strike the right balance between the capabilities of generators and discriminators. The relative discriminator is used to calculate the loss value from adversarial neural network. Compared with the mainstream super-resolution reconstruction algorithm on the basic of Set5, Set14 and BSD100 datasets, the experimental results show that the proposed approach achieves consistently better visual quality with more sharper, realistic and high-frequency textures than prior methods, and is effective in diversified image generation applications.

Key words: super-resolution, generative adversarial network, attention augmented, deep residual network, diversity