用总变差方法提升显微图像质量，有效去噪并分离信号成分。
Total Variation-Based Image Decomposition and Denoising for Microscopy Images
- 基于总变差框架分解图像，分离并去除噪声与干扰信号。
- TGV-L1在去噪任务中表现最优，Huber-ROF更具灵活性。
- 适用于多种显微技术，代码开源便于实验集成。
实验获取的显微图像不可避免地受噪声及其他非相关信号影响，降低图像质量并可能掩盖关键特征。随着成像速度提升，现代去噪与修复方法变得尤为重要。本研究针对原子力显微镜（AFM）、扫描隧道显微镜（STM）和扫描电子显微镜（SEM）等多种显微技术获取的图像，提出基于总变差（TV）的图像分解与去噪流程。通过提取并从原始图像中减去干扰成分，或直接进行去噪处理实现图像恢复。评估了TV-L1、Huber-ROF和TGV-L1三种方法的表现，结果表明Huber-ROF最具灵活性，而TGV-L1在去噪方面最为适用。研究显示该方法具有更广泛的应用前景，不仅限于上述三种显微技术。用于本研究的Python代码已作为AiSurf的一部分公开，可集成至实验采集流程，也可用于处理已有图像。
原文摘要 · Abstract (English)
Experimentally acquired microscopy images are unavoidably affected by the presence of noise and other unwanted signals, which degrade their quality and might hide relevant features. With the recent increase in image acquisition rate, modern denoising and restoration solutions become necessary. This study focuses on image decomposition and denoising of microscopy images through a workflow based on total variation (TV), addressing images obtained from various microscopy techniques, including atomic force microscopy (AFM), scanning tunneling microscopy (STM), and scanning electron microscopy (SEM). Our approach consists in restoring an image by extracting its unwanted signal components and subtracting them from the raw one, or by denoising it. We evaluate the performance of TV-$L^1$, Huber-ROF, and TGV-$L^1$ in achieving this goal in distinct study cases. Huber-ROF proved to be the most flexible one, while TGV-$L^1$ is the most suitable for denoising. Our results suggest a wider applicability of this method in microscopy, restricted not only to STM, AFM, and SEM images. The Python code used for this study is publicly available as part of AiSurf. It is designed to be integrated into experimental workflows for image acquisition or can be used to denoise previously acquired images.
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