用小波驱动的GAN无监督去噪，保留显微图像高频细节。
QWD-GAN: Quality-aware Wavelet-driven GAN for Unsupervised Medical Microscopy Images Denoising
- 基于小波变换设计多尺度自适应生成器，提升噪声分离能力。
- 双分支判别器融合原始特征与差异感知特征，去噪效果优于现有方法。
- 无需配对数据，适合临床显微图像处理，可嵌入多种GAN框架。
图像去噪在生物医学和显微成像中至关重要，尤其在获取宽场荧光染色图像时面临多重挑战：成像条件受限、噪声类型复杂、算法适应性差及临床应用需求高。尽管已有众多深度学习去噪方法表现良好，但在保留图像细节、提升算法效率和增强临床可解释性方面仍需改进。本文提出一种基于生成对抗网络（GAN）的无监督图像去噪方法，引入基于小波变换的多尺度自适应生成器，以及融合差异感知特征图与原始特征的双分支判别器。在多个生物医学显微图像数据集上的实验结果表明，所提模型在去噪性能上达到当前最优水平，尤其在高频率信息保留方面表现突出。此外，双分支判别器可无缝适配多种GAN框架。该质量感知的小波驱动去噪模型命名为QWD-GAN。
原文摘要 · Abstract (English)
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.
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