关键词: 超分辨率重建, 生成对抗网络, 残差密集网络, PatchGAN

Abstract:

Recently, the generative adversarial networks have shown good potential in the generation of constrained image, which makes it suitable for image super-resolution reconstruction. In view of the problem of low utilization of feature information in image super-resolution reconstruction algorithm based on convolution neural network, this paper proposes a super-resolution reconstruction algorithm for residual dense generative adversarial network on the basis of the framework of generative adversarial network. The algorithm defines the generator network and the discriminator network. Through constructing the residual dense network as the generator network and PatchGAN as the discriminator network, it solves the problem of the low utilization rate of feature information in the super-resolution algorithm based on convolution neural network and the problem of the slow convergence of the generative adversarial network. The reconstruction algorithm is compared with the mainstream super-resolution reconstruction algorithm on the basis of Set5 and other standard datasets. The experimental results show that the proposed algorithm can effectively improve the utilization rate of feature information, restore the details of low-resolution images, and improve the quality of image reconstruction.

Key words: super-resolution reconstruction, generative adversarial networks, residual dense network, PatchGAN