Coherent Diffractive Imaging Using Randomly Coded Masks

Matthew H. Seaberg (1, 2, 3) , Alexandre d'Aspremont (1, 2) , Joshua J. Turner (3)

1 LIENS - Laboratoire d'informatique de l'école normale supérieure
2 SIERRA - Statistical Machine Learning and Parsimony
3 SLAC - SLAC National Accelerator Laboratory

Coherent diffractive imaging (CDI) provides new opportunities for high resolution X-ray imaging with simultaneous amplitude and phase contrast. Extensions to CDI broaden the scope of the technique for use in a wide variety of experimental geometries and physical systems. Here, we experimentally demonstrate a new extension to CDI that encodes additional information through the use of a series of randomly coded masks. The information gained from the few additional diffraction measurements removes the need for typical object-domain constraints; the algorithm uses prior information about the masks instead. The experiment is performed using a laser diode at 532.2 nm, enabling rapid prototyping for future X-ray synchrotron and even free electron laser experiments. Diffraction patterns are collected with up to 15 different masks placed between a CCD detector and a single sample. Phase retrieval is performed using a convex relaxation routine known as "PhaseCut" followed by a variation on Fienup's input-output algorithm. The reconstruction quality is judged via calculation of phase retrieval transfer functions as well as by an object-space comparison between reconstructions and a lens-based image of the sample. The results of this analysis indicate that with enough masks (in this case 3 or 4) the diffraction phases converge reliably, implying stability and uniqueness of the retrieved solution.

Domaines: Recherche opérationnelle [math.OC]; Intelligence artificielle [cs.AI]; Machine Learning [stat.ML]

Matthew H. Seaberg, Alexandre d'Aspremont, Joshua J. Turner. Coherent Diffractive Imaging Using Randomly Coded Masks. Applied Physics Letters, 2015, 107 (23), pp.231103. ⟨hal-01239315⟩