# Joint Optimization of Hadamard Sensing and Reconstruction in Compressed Sensing Fluorescence Microscopy

arXiv: https://arxiv.org/abs/2105.07961 · HTML: https://mlsanity.com/paper/2105.07961/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images](https://mlsanity.com/paper/1812.10366/) (2018)
- [Unrolled Optimization with Deep Priors](https://mlsanity.com/paper/1705.08041/) (2017)
- [Learning Sensor Multiplexing Design through Back-propagation](https://mlsanity.com/paper/1605.07078/) (2016)
- [Extending LOUPE for K-space Under-sampling Pattern Optimization in Multi-coil MRI](https://mlsanity.com/paper/2007.14450/) (2020)
- [Data-Driven Design for Fourier Ptychographic Microscopy](https://mlsanity.com/paper/1904.04175/) (2019)
- [Neural Network-based Reconstruction in Compressed Sensing MRI Without Fully-sampled Training Data](https://mlsanity.com/paper/2007.14979/) (2020)
- [Learning a Probabilistic Strategy for Computational Imaging Sensor Selection](https://mlsanity.com/paper/2003.10424/) (2020)
- [Training Convolutional Neural Networks and Compressed Sensing End-to-End for Microscopy Cell Detection](https://mlsanity.com/paper/1810.03075/) (2018)
- [Optimal compressive multiphoton imaging at depth using single-pixel detection](https://mlsanity.com/paper/1907.02272/) (2019)

## Built on by (most cited first, 1 of 1)

- [Computing Multiple Image Reconstructions with a Single Hypernetwork](https://mlsanity.com/paper/2202.11009/) (2022)

## Abstract (arXiv)

Compressed sensing fluorescence microscopy (CS-FM) proposes a scheme whereby less measurements are collected during sensing and reconstruction is performed to recover the image. Much work has gone into optimizing the sensing and reconstruction portions separately. We propose a method of jointly optimizing both sensing and reconstruction end-to-end under a total measurement constraint, enabling learning of the optimal sensing scheme concurrently with the parameters of a neural network-based reconstruction network. We train our model on a rich dataset of confocal, two-photon, and wide-field microscopy images comprising of a variety of biological samples. We show that our method outperforms several baseline sensing schemes and a regularized regression reconstruction algorithm.