PSRGAN: Perception-Design-Oriented Image Super Resolution Generative Adversarial Network

Abstract

Among recent state-of-the-art realistic image super-resolution (SR) intelligent algorithms, generative adversarial networks (GANs) have achieved impressive visual performance. However, there has been the problem of unsatisfactory perception of super-scored pictures with unpleasant artifacts. To address this issue and further improve visual quality, we proposed a perception-design-oriented PSRGAN with double perception turbos for real-world SR. The first-perception turbo in the generator network has a three-level perception structure with different convolution kernel sizes, which can extract multi-scale features from four 14 size sub-images sliced by original LR image. The slice operation expands adversarial samples to four and could alleviate artifacts during GAN training. The extracted features will be eventually concatenated in later 3 × 2 upsampling processes through pixel shuffle to restore SR image with diversified delicate textures. The second-perception turbo in discriminators has cascaded perception turbo blocks (PTBs), which could further perceive multi-scale features at various spatial relationships and promote the generator to restore subtle textures driven by GAN. Compared with recent SR methods (BSRGAN, real-ESRGAN, PDM_SR, SwinIR, LDL, etc.), we conducted an extensive test with a ×4 upscaling factor on various datasets (OST300, 2020track1, RealSR-Canon, RealSR-Nikon, etc.). We conducted a series of experiments that show that our proposed PSRGAN based on generative adversarial networks outperforms current state-of-the-art intelligent algorithms on several evaluation metrics, including NIQE, NRQM and PI. In terms of visualization, PSRGAN generates finer and more natural textures while suppressing unpleasant artifacts and achieves significant improvements in perceptual quality.