QWD-GAN: Quality-aware Wavelet-driven GAN for Unsupervised Medical Microscopy Images Denoising


Qijun Yang (The University of Manchester), Yating Huang (The University of Manchester), Lintao Xiang (The University of Manchester), Hujun Yin (The University of Manchester)
The 35th British Machine Vision Conference
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Abstract

Image denoising plays a critical role in biomedical and microscopy imaging, especially when acquiring wide-field fluorescence-stained images. This task faces challenges in multiple fronts, including limitations in image acquisition conditions, complex noise types, algorithm adaptability, and clinical application demands. Although many deep learning-based denoising techniques have demonstrated promising results, further improvements are needed in preserving image details, enhancing algorithmic efficiency, and increasing clinical interpretability. We propose an unsupervised image denoising method based on a Generative Adversarial Network (GAN) architecture. The approach introduces a multi-scale adaptive generator based on wavelet transform and a dual-branch discriminator that integrates difference perception feature maps with original features. Experimental results on multiple biomedical microscopy image datasets show that the proposed model achieves state-of-the-art denoising performance, particularly excelling in the preservation of high-frequency information. Furthermore, the dual-branch discriminator is seamlessly compatible with various GAN frameworks. This quality-aware, wavelet-driven GAN denoising model is termed as QWD-GAN.

Citation

@inproceedings{Yang_2025_BMVC,
author    = {Qijun Yang and Yating Huang and Lintao Xiang and Hujun Yin},
title     = {QWD-GAN: Quality-aware Wavelet-driven GAN for Unsupervised Medical Microscopy Images Denoising},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_887/paper.pdf}
}


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