Phase retrieval for imaging problems

Abstract

We study convex relaxation algorithms for phase retrieval on imaging problems. We show that structural assumptions on the signal and the observations, such as sparsity, smoothness or positivity, can be exploited to both speed-up convergence and improve recovery performance. We detail experimental results in molecular imaging problems simulated from PDB data.

Significantly expanded experiments

References in corpus (1)

• Templates for Convex Cone Problems with Applications to Sparse Signal Recovery

Cited by in corpus (3)

• Low Rank Phase Retrieval
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