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

TL;DR

This paper introduces a joint optimization approach for sensing and reconstruction in compressed sensing fluorescence microscopy, leveraging neural networks to improve image recovery under measurement constraints.

Contribution

It presents the first end-to-end learning framework that simultaneously optimizes sensing and reconstruction in CS-FM, outperforming traditional methods.

Findings

Outperforms baseline sensing schemes

Achieves better reconstruction quality

Effective on diverse biological microscopy images

Abstract

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.