Image Denoising Based on Frequency Decomposition Generative Adversarial Network

Abstract

In wireless communication scenarios, especially in low-bandwidth or noisy transmission conditions, image data is often degraded by interference during acquisition or transmission. To address this, we proposed Wasserstein frequency generative adversarial networks (WF-GAN), a frequency-aware denoising model based on wavelet transformation. By decomposing images into four frequency sub-bands, the model separates low-frequency contour information from high-frequency texture details. Contour guidance is applied to preserve structural integrity, while adversarial training enhances texture fidelity in the high-frequency bands. A joint loss function, incorporating frequency-domain loss and perceptual loss, is designed to reduce detail degradation during denoising. Experiments on public image datasets, with Gaussian noise applied to simulate wireless communication interference, demonstrate that WF-GAN consistently outperforms both traditional and deep learning-based denoising methods in terms of visual quality and quantitative metrics. These results highlight its potential for robust image processing in wireless communication systems.

Keywords

image denoising / deep learning / wavelet transform / edge guidance