  In this paper, we consider the problem of super-resolution recons-truction.
This is a hot topic because super-resolution reconstruction has a wide range of
applications in the medical field, remote sensing monitoring, and criminal
investigation. Compared with traditional algorithms, the current
super-resolution reconstruction algorithm based on deep learning greatly
improves the clarity of reconstructed pictures. Existing work like
Super-Resolution Using a Generative Adversarial Network (SRGAN) can effectively
restore the texture details of the image. However, experimentally verified that
the texture details of the image recovered by the SRGAN are not robust. In
order to get super-resolution reconstructed images with richer high-frequency
details, we improve the network structure and propose a super-resolution
reconstruction algorithm combining wavelet transform and Generative Adversarial
Network. The proposed algorithm can efficiently reconstruct high-resolution
images with rich global information and local texture details. We have trained
our model by PyTorch framework and VOC2012 dataset, and tested it by Set5,
Set14, BSD100 and Urban100 test datasets.
