Adversarial sensing is a self-supervised, learning-based approach for solving inverse problems with stochastic forward models. The basic idea behind adversarial sensing is that one can use a discriminator to compare the distributions of predicted and observed measurements. The feedback from the discriminator thus allows one to reconstruct a signal from observations from stochastic forward models without solving for any of the forward model’s unknown latent variables. While adversarial sensing requires no training data, it can be modified to incorporate pretrained deep generative models for use as priors. This talk highlights some of our recent work on applying adversarial sensing to imaging through atmospheric turbulence and to long-range sub-diffraction limited imaging with Fourier ptychography.
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