Abstract

Deep Learning presents a promising opportunity to design computational architectures for solving inverse problems. In this talk, we will present several approaches for performing computational imaging in this fashion, and discuss their relative merits especially with respect to image fidelity and robustness to noise.

© 2018 The Author(s)

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