Deep neural networks have been successfully applied to constrained object priors and parameter optimization. Here, we propose a novel learning-free deep neural network architecture to tackle uncertain system optimization. This blind system constraint deep neural network(BlindNet) need not to know all the parameters of the system and can simultaneously acquire the desired image and system parameters. In order to do so, we showed that the BlindNet can perform phase retrieval on the diffraction pattern with unknown diffraction distance.
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