Adversarial sensing: a learning-based approach to inverse problems with stochastic forward models

Brandon Y. Feng; Mingyang Xie; Christopher A. Metzler

doi:10.1117/12.2634290

3 October 2022 Adversarial sensing: a learning-based approach to inverse problems with stochastic forward models

Brandon Y. Feng, Mingyang Xie, Christopher A. Metzler

Proceedings Volume 12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; 122040G (2022) https://doi.org/10.1117/12.2634290
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States

Abstract

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 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 paper highlights some of our recent work on applying adversarial sensing to imaging through turbulence and to long-range sub-diffraction limited imaging with Fourier ptychography. For a longer and more detailed discussion of our methods please see.¹

Conference Presentation

Citation Download Citation

Brandon Y. Feng, Mingyang Xie, and Christopher A. Metzler "Adversarial sensing: a learning-based approach to inverse problems with stochastic forward models", Proc. SPIE 12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, 122040G (3 October 2022); https://doi.org/10.1117/12.2634290

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