Edge-Enhanced GAN for Remote Sensing Image Superresolution

The current superresolution (SR) methods based on deep learning have shown remarkable comparative advantages but remain unsatisfactory in recovering the high-frequency edge details of the images in noise-contaminated imaging conditions, e.g., remote sensing satellite imaging. In this paper, we propose a generative adversarial network (GAN)-based edge-enhancement network (EEGAN) for robust satellite image SR reconstruction along with the adversarial learning strategy that is insensitive to noise. In particular, EEGAN consists of two main subnetworks: an ultradense subnetwork (UDSN) and an edge-enhancement subnetwork (EESN). In UDSN, a group of 2-D dense blocks is assembled for feature extraction and to obtain…

• NN
  National Natural Science Foundation of China

  Awards: U1736206, 61501413, 61671332, 61502354, 61671336
• HT
  Hubei Technological Innovation Special Fund

  Award: 2017AAA123