ESRGAN-DP: Enhanced super-resolution generative adversarial network with adaptive dual perceptual loss

Abstract

The proposal of perceptual loss solves the over-smoothing problem of images caused by pixel-wise loss and improves the visual quality of the images, but it also inevitably produces a large number of artifacts and distortions in images. The reason for this phenomenon is that the perceptual features only rely on a single pre-trained visual geometry group (VGG) network, which results in the features of the image being unable to be fully extracted, thus limiting the reasoning ability of the model. To fundamentally reduce the generation of artifacts and distortions, this paper proposes the Dual Perceptual Loss (DP Loss). First, we improve the perceptual feature extraction method so that it no longer only extracts single-type VGG features. In addition, a residual network (ResNet) feature that has a complementary relationship with the VGG feature can also be extracted. Then, we propose a dynamic weighting method to eliminate the magnitude difference between perceptual losses. Finally, to obtain the excellent effect of image reconstruction, enhanced super-resolution generative adversarial network (ESRGAN) with strong learning capability is used in this paper to adapt the complexity of DP Loss. The abundant experimental studies and evaluations are conducted on benchmark datasets. Results are encouraging and better than those previously reported on these datasets. The code is available at https://github.com/Sunny6-6-6/ESRGAN-DP.