Total Variation-Based Image Decomposition and Denoising for Microscopy Images
Journal:
arXiv
Published Date:
May 13, 2025
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.
