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 the 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. The proposed quality-aware, wavelet-driven GAN denoising model is termed as QWD-GAN.

翻译：图像去噪在生物医学和显微成像中起着至关重要的作用，尤其是在获取宽场荧光染色图像时。该任务面临多方面的挑战，包括图像采集条件的限制、复杂的噪声类型、算法的适应性以及临床应用需求。尽管许多基于深度学习的去噪技术已展现出良好的效果，但在保留图像细节、提升算法效率以及增强临床可解释性方面仍需进一步改进。我们提出了一种基于生成对抗网络架构的无监督图像去噪方法。该方法引入了一种基于小波变换的多尺度自适应生成器，以及一个将差异感知特征图与原始特征相结合的双分支判别器。在多个生物医学显微图像数据集上的实验结果表明，所提模型实现了最先进的去噪性能，尤其在保留高频信息方面表现优异。此外，该双分支判别器能够无缝兼容多种GAN框架。所提出的质量感知小波驱动GAN去噪模型被命名为QWD-GAN。