Joint Optimization of Hadamard Sensing and Reconstruction in Compressed Sensing Fluorescence Microscopy
This work addresses the challenge of improving image recovery efficiency in fluorescence microscopy for biological imaging, representing an incremental advance by integrating previously separate optimizations.
The paper tackles the problem of jointly optimizing sensing and reconstruction in compressed sensing fluorescence microscopy under a total measurement constraint, resulting in a method that outperforms baseline sensing schemes and a regularized regression reconstruction algorithm.
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.