Progressive Upsampling Generative Adversarial Network with Collaborative Attention for Single-Image Super-Resolution

Single-image super-resolution (SISR) is an essential low-level visual task that aims to produce high-resolution images from low-resolution inputs. However, most existing SISR methods heavily rely on ideal degradation kernels and rarely consider the actual noise distribution. To tackle these issues, this paper presents a progressive upsampling generative adversarial network with collaborative attention mechanism called PUGAN. Specifically, the residual multiscale blocks (RMBs) based on stacked mixed-pooling multiscale structures (MPMSs) is designed to make full use of multiscale global-local hierarchical features, and the frequency collaborative attention mechanism (CAM) is used to fully dig up high- and low-frequency characteristics. Meanwhile, we design a progressive upsampling strategy to guide the model's learning better while reducing the model's complexity. Finally, the discriminator is also used to evaluate the reconstructed high-resolution images for balancing super-resolution reconstruction and detail enhancement. Our PUGAN can yield comparable PSNR/SSIM/LPIPS values for the NTIRE 2020, Urban 100, and B100 datasets, whose values are 33.987/0.9673/0.1210, 32.966/0.9483/0.1431, and 33.627/0.9546/0.1354 for the scale factor of ×2 as well as 26.349/0.8721/0.1975, 26.110/0.8614/0.1983, and 26.306/0.8803/0.1978 for the scale factor of ×4, respectively. Extensive experiments demonstrate that our PUGAN outperforms state-of-the-art SISR methods in qualitative and quantitative assessments for the SISR task. Additionally, our PUGAN shows the potential benefits to pathological image super-resolution.