Work

Image super-resolution method using a generative adversarial network incorporating attention and residual density

Year: 2025

Type: article

Abstract: Image super-resolution reconstruction is currently gaining huge attention because of its very promising applications. However, there are still problems such as loss of detailed features and low signal-to-noise ratio. To address this problem, we propose a generative adversarial network that incorporates attention and residual density. Firstly, the network makes full use of the different layers of features for fusion and prevents gradient decay through the residual dense module. Then through the attention gate structure, the noisy information is suppressed and the signal-to-noise ratio value is improved. The experimental results show that the method proposed is improved in peak signal-to-noise ratio and structural similarity index measure compared to other comparative methods, which proves the advancement of the method proposed.

Source: Scientific Reports

DOI: 10.1038/s41598-025-28677-0

Authors: Qiong Zhang, Yiliu Hang, Hui Zhang, Xi Lu, Jianlin Qiu

Institution: Nantong Institute of Technology

Language: English

Topic: Advanced Image Processing Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,690

Funders: Government of Jiangsu Province, Nantong Science and Technology Bureau, Major Basic Research Project of the Natural Science Foundation of the Jiangsu Higher Education Institutions