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

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.