Work

Generative Adversarial Networks With Noise Optimization and Pyramid Coordinate Attention for Robust Image Denoising

Year: 2025

Type: article

Abstract: Image denoising is a significant challenge in computer vision. While many models perform well in low‐noise environments, their denoising capabilities are relatively weak under high‐noise conditions. In addition, these models often overlook the robustness issues under adversarial attacks, leading to a marked decrease in denoising stability when facing malicious attacks. To address the challenges of achieving consistently high‐quality denoising in both high‐noise and low‐noise environments, adapting to various complex scenarios with high robustness, and enhancing the model’s resilience against attacks, we propose the NOP‐GAN, a powerful image denoising model. This model modifies the GAN architecture by integrating a U‐Net with a pyramid coordinate attention mechanism and a noise optimization algorithm into a generator of the GAN. Experimental results demonstrate that the NOP‐GAN possesses superior performance in denoising tasks and robustness against adversarial attacks.

Source: International Journal of Intelligent Systems

DOI: 10.1155/int/1546016

Authors: Min‐Ling Zhu, Jia‐Hua Yuan, En Kong, Liang‐Liang Zhao, Li Xiao, Dong‐Bing Gu

Institutions: Beijing Information Science & Technology University, Beijing University of Posts and Telecommunications, University of Essex

Language: English

Topic: Image and Signal Denoising Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,700

Funders: National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities