Total Variation-Based Image Decomposition and Denoising for Microscopy Images

TL;DR

This paper presents a total variation-based workflow for decomposing and denoising microscopy images, improving image quality across various microscopy techniques with publicly available Python tools.

Contribution

It introduces a TV-based method for microscopy image decomposition and denoising, demonstrating its effectiveness and flexibility across different microscopy modalities.

Findings

Huber-ROF is the most flexible method.

TGV-$L^1$ is most effective for denoising.

The approach is applicable beyond STM, AFM, and SEM images.

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.