Improving the resolution of microscope by deconvolution after dense scan

TL;DR

This paper introduces a computational method called Deconvolution after Dense Scan (DDS) that enhances super-resolution microscope images by combining dense scanning with deconvolution, significantly improving resolution without major hardware changes.

Contribution

The paper presents a novel algorithmic approach for improving microscope resolution through dense scanning and deconvolution, requiring minimal modifications to existing equipment.

Findings

Simulation results demonstrate improved resolution in microscopy images.

The method effectively reduces optical uncertainty in peripheral areas.

No significant hardware modifications are needed.

Abstract

Super-resolution microscopes (such as STED) illuminate samples with a tiny spot, and achieve very high resolution. But structures smaller than the spot cannot be resolved in this way. Therefore, we propose a technique to solve this problem. It is termed "Deconvolution after Dense Scan (DDS)". First, a preprocessing stage is introduced to eliminate the optical uncertainty of the peripheral areas around the sample's ROI (Region of Interest). Then, the ROI is scanned densely together with its peripheral areas. Finally, the high resolution image is recovered by deconvolution. The proposed technique does not need to modify the apparatus much, and is mainly performed by algorithm. Simulation experiments show that the technique can further improve the resolution of super-resolution microscopes.