Implementation of reflection matrix microscopy: An algorithm perspective

TL;DR

This paper presents advanced algorithms for reflection matrix microscopy that significantly improve processing speed and image reconstruction quality, enabling better deep-tissue imaging in biological research.

Contribution

It introduces novel reflection matrix processing algorithms, including logical indexing, power iterations, and low-frequency blocking, to enhance RMM performance.

Findings

Processing speed increased by orders of magnitude

Improved 3D image reconstruction quality

Facilitates deep-tissue imaging applications

Abstract

Over the past decade, reflection matrix microscopy (RMM) and advanced image reconstruction algorithms have emerged to address the fundamental imaging depth limitations of optical microscopy in thick biological tissues and complex media. In this study, we introduce significant advancements in reflection matrix processing algorithms, including logical indexing, power iterations, and low-frequency blocking. These enhance the processing speed of aperture synthesis, 3D image reconstruction, and aberration correction by orders of magnitude. Detailed algorithm implementations, along with experimental data, are provided to facilitate the widespread adoption of RMM in various deep-tissue imaging applications.