Improving the resolution of microscope by deconvolution after dense scan
This work addresses a limitation in super-resolution microscopy for researchers, but it appears incremental as it builds on existing deconvolution methods with algorithmic enhancements.
The paper tackles the problem of resolving structures smaller than the illumination spot in super-resolution microscopes by proposing Deconvolution after Dense Scan (DDS), which involves preprocessing, dense scanning, and deconvolution to recover high-resolution images without major hardware modifications, with simulation experiments showing resolution improvement.
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.