Phase retrieval for imaging problems

Fajwel Fogel (1, 2) , Irène Waldspurger (1) , Alexandre d'Aspremont (1, 2)

1 LIENS - Laboratoire d'informatique de l'école normale supérieure
2 SIERRA - Statistical Machine Learning and Parsimony

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.

Domaines: Optimisation et contrôle [math.OC]

Fajwel Fogel, Irène Waldspurger, Alexandre d'Aspremont. Phase retrieval for imaging problems. Mathematical Programming Computation, 2016, 8 (3), pp.311-335. ⟨10.1007/s12532-016-0103-0⟩. ⟨hal-00907529⟩