The Potential of Subsampling and Inpainting for Fast Low-Dose Cryo FIB-SEM Imaging and Tomography

TL;DR

This paper demonstrates that subsampling and inpainting techniques can enable fast, low-dose cryo FIB-SEM imaging and tomography, reducing data acquisition time and damage while maintaining image quality.

Contribution

The study introduces new methods for 3D data handling using dictionary learning and inpainting algorithms, validated with experimental data in cryo FIB-SEM.

Findings

Subsampling and inpainting improve imaging speed and reduce beam damage.

New algorithms effectively recover high-quality images from limited data.

Experimental validation confirms practical utility in cryo FIB-SEM.

Abstract

Traditional image acquisition for cryo focused ion-beam scanning electron microscopy tomography often sees thousands of images being captured over a period of many hours, with immense data sets being produced. When imaging beam sensitive materials, these images are often compromised by additional constraints related to beam damage and the devitrification of the material during imaging, which renders data acquisition both costly and unreliable. Subsampling and inpainting are proposed as solutions for both of these aspects, allowing fast and low-dose imaging to take place in the FIB-SEM without an appreciable low in image quality. In this work, experimental data is presented which validates subsampling and inpainting as a useful tool for convenient and reliable data acquisition in a FIB-SEM, with new methods of handling 3-dimensional data being employed in context of dictionary learning and inpainting algorithms using a newly developed microscope control software and data recovery algorithm.