EE Zoom Seminar: Score-based diffusion priors for multi-target detection

Electrical Engineering Systems ZOOM Seminar

Speaker: Alon Zabatani

M.Sc. student under the supervision of Dr. Tamir Bendory

Sunday, 29^th September 2024, at 14:00

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Score-based diffusion priors for multi-target detection

Abstract

Multi-target detection (MTD) is the problem of estimating an image from a large, noisy measurement that contains randomly translated and rotated copies of the image. Motivated by the single-particle cryo-electron microscopy technology, we design data-driven diffusion priors for the MTD problem, derived from score-based stochastic differential equations models. We then integrate the prior into the approximate expectation-maximization algorithm. In particular, our method alternates between an expectation step that approximates the expected log-likelihood and a maximization step that balances the approximated log-likelihood with the learned log-prior. We show on two datasets that adding the data-driven prior substantially reduces the estimation error, in particular in high noise regimes.

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