  Among the major remaining challenges for single image super resolution (SISR)
is the capacity to recover coherent images with global shapes and local details
conforming to human vision system. Recent generative adversarial network (GAN)
based SISR methods have yielded overall realistic SR images, however, there are
always unpleasant textures accompanied with structural distortions in local
regions. To target these issues, we introduce the gradient branch into the
generator to preserve structural information by restoring high-resolution
gradient maps in SR process. In addition, we utilize a U-net based
discriminator to consider both the whole image and the detailed per-pixel
authenticity, which could encourage the generator to maintain overall coherence
of the reconstructed images. Moreover, we have studied objective functions and
LPIPS perceptual loss is added to generate more realistic and natural details.
Experimental results show that our proposed method outperforms state-of-the-art
perceptual-driven SR methods in perception index (PI), and obtains more
geometrically consistent and visually pleasing textures in natural image
restoration.
