# Compressive Image Scanning Microscope

arXiv: https://arxiv.org/abs/2307.09841 · HTML: https://mlsanity.com/paper/2307.09841/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 19 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [Total Variation Applications in Computer Vision](https://mlsanity.com/paper/1603.09599/) (2016)
- [Reconstructing the Image Scanning Microscopy Dataset: an Inverse Problem](https://mlsanity.com/paper/2211.12510/) (2022)
- [Total variation vs L1 regularization: a comparison of compressive sensing optimization methods for chemical detection](https://mlsanity.com/paper/1906.10603/) (2019)
- [Review of Algorithms for Compressive Sensing of Images](https://mlsanity.com/paper/1908.01642/) (2019)

## Abstract (arXiv)

We present a novel approach to implement compressive sensing in laser scanning microscopes (LSM), specifically in image scanning microscopy (ISM), using a single-photon avalanche diode (SPAD) array detector. Our method addresses two significant limitations in applying compressive sensing to LSM: the time to compute the sampling matrix and the quality of reconstructed images. We employ a fixed sampling strategy, skipping alternate rows and columns during data acquisition, which reduces the number of points scanned by a factor of four and eliminates the need to compute different sampling matrices. By exploiting the parallel images generated by the SPAD array, we improve the quality of the reconstructed compressive-ISM images compared to standard compressive confocal LSM images. Our results demonstrate the effectiveness of our approach in producing higher-quality images with reduced data acquisition time and potential benefits in reducing photobleaching.