Abstract:Recently deep neural networks (DNN) has shown the great capability of solving various inverse problems in computational optical imaging. Conventionally, DNN should be trained by a large set of paired or unpaired data. The most critical issue with this paradigm is that the neural network inference has no physical interpretation or limited generalization. In order to resolve these issues, one solution is to incorporate the physics of the problems in hand into the training of DNN, resulting in a novel framework t… Show more
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