In scanning ptychography, an unknown specimen is illuminated by a localised illumination function resulting in an exit-wave whose intensity is observed in the far-field. A ptychography dataset is a series of these observations, each of which is obtained by shifting the illumination function to a different position relative to the specimen with neighbouring illumination regions overlapping. Given a ptychographic data set, the blind ptychography problem is to simultaneously reconstruct the specimen, illumination function, and relative phase of the exit-wave. In this talk I will discuss an optimisation framework which reveals current state-of-the-art reconstruction methods in ptychography as (non-convex) alternating minimization-type algorithms. Within this framework, we provide a proof of global convergence to critical points using the Kurdyka-Lojasiewicz property.
This is joint work with R. Hesse, D.R. Luke, and S. Sabach.
About the speaker: Matthew Tam is a PhD candidate working in optimisation at the Centre for Computer-Assisted Research Mathematics and its Applications at the University of Newcastle, supervised by Laureate Professor Jonathan Borwein. Prior to staring a PhD, he studied mathematics and chemistry, also from the University of Newcastle. Matthew’s thesis is concerned with the theory and application of the family of so-called ‘iterative projection algorithms’ which can be used to solve a variety of optimisation and reconstruction problems.
How to participate in this seminar:
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