Kavli Affiliate: Chris Xu
| First 5 Authors: Alan Q. Wang, Aaron K. LaViolette, Leo Moon, Chris Xu, Mert R. Sabuncu
| Summary:
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.
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