Progressive Upsampling Generative Adversarial Network with Collaborative Attention for Single-Image Super-Resolution
Haoxiang Lu, Jing Zhang, Mengyuan Jing, Ziming Wang, Wenhao Wang
TL;DR
This paper introduces a new AI model called PUGAN that improves the quality of low-resolution images by using advanced attention mechanisms and progressive upsampling techniques.
Contribution
The novel PUGAN model combines collaborative attention and progressive upsampling for better single-image super-resolution performance.
Findings
PUGAN achieves comparable PSNR/SSIM/LPIPS values on benchmark datasets like NTIRE 2020 and Urban 100.
The model outperforms existing state-of-the-art methods in both qualitative and quantitative assessments.
PUGAN shows potential for applications in pathological image super-resolution.
Abstract
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…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Image and Video Quality Assessment · Advanced Image Fusion Techniques