Total Variation-Based Image Decomposition and Denoising for Microscopy Images

2025-05-13

AI Summary

Microscopy images (AFM/STM/SEM) are commonly degraded by noise and spurious signals, limiting high-throughput imaging quality. To address this, we propose a general-purpose image decomposition and denoising workflow based on total variation (TV). We systematically evaluate three TV-based models—TV-L¹, Huber-ROF, and TGV-L¹—across multimodal microscopy data. Huber-ROF demonstrates superior generalizability, while TGV-L¹ achieves the optimal trade-off between denoising accuracy and structural fidelity. Notably, this work presents the first cross-platform adaptation of TGV-L¹ to AFM, STM, and SEM imaging modalities. The method is integrated into the open-source AiSurf framework, enabling both real-time acquisition embedding and offline batch processing. Experimental results show substantial improvements in signal-to-noise ratio and fine-detail preservation. All source code is publicly available.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Denoising microscopy images affected by noise and unwanted signals
• Comparing TV-based methods for image decomposition and restoration
• Extending applicability beyond STM, AFM, and SEM microscopy techniques

Innovation

• Total variation-based image decomposition and denoising
• Evaluates TV-L1, Huber-ROF, and TGV-L1 methods
• Python code integrated into experimental workflows