Investigating Loss Functions for Extreme Super-Resolution

The performance of image super-resolution (SR) has been greatly improved by using convolutional neural networks. Most of the previous SR methods have been studied up to ×4 upsampling, and few were studied for ×16 upsampling. The general approach for perceptual ×4 SR is using GAN with VGG based perceptual loss, however, we found that it creates inconsistent details for perceptual ×16 SR. To this end, we have investigated loss functions and we propose to use GAN with LPIPS [23] loss for perceptual extreme SR. In addition, we use U-net structure discriminator [14] together to consider both the global and local context of an input image. Experimental results show that our method outperforms the conventional perceptual loss, and we achieved second and first place in the LPIPS and PI measures respectively for NTIRE 2020 perceptual extreme SR challenge.