S
Stefano Marchesini
Researcher at Lawrence Berkeley National Laboratory
Publications - 172
Citations - 12678
Stefano Marchesini is an academic researcher from Lawrence Berkeley National Laboratory. The author has contributed to research in topics: Diffraction & Ptychography. The author has an hindex of 49, co-authored 167 publications receiving 11520 citations. Previous affiliations of Stefano Marchesini include University of California, Berkeley & French Alternative Energies and Atomic Energy Commission.
Papers
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Journal ArticleDOI
X-ray microscopy by phase-retrieval methods at the advanced light source
Malcolm R. Howells,Henry N. Chapman,Stefan P. Hau-Riege,H. He,Stefano Marchesini,John C. H. Spence,John C. H. Spence,Uwe Weierstall +7 more
Abstract: We report our experiments in soft x-ray coherent diffraction leading to reconstructed images via phase retrieval methods. We describe the history and principles of the method and the technical systems we have used to implement it. The main requirement is to have a sufficiently isolated sample.
Proceedings ArticleDOI
Binary pseudorandom array test standard optimized for characterization of large field-of-view optical interferometers
Valeriy V. Yashchuk,Sergey A. Babin,Stefano Cabrini,Weilun Chao,Ulf Griesmann,Ian Lacey,Stefano Marchesini,Keiko Munechika,Carlos Pina-Hernandez,Allen Leonid Roginsky +9 more
TL;DR: Compared with the previous coded-aperture based design, the improved, ‘highly randomized’ BPRA pattern of the new test standard provides better accuracy and reliability of instrument MTF and aberration characterization, and enables operation optimization of large aperture optical interferometers.
Book ChapterDOI
Sparse Matrix-Based HPC Tomography
TL;DR: The proposed software is designed to provide flexible, portable and high-performance operators that can be used for continuous integration at different production environments, but also for prototyping new experimental settings or for algorithmic development.
Journal ArticleDOI
Denoising Poisson phaseless measurements via orthogonal dictionary learning.
Huibin Chang,Stefano Marchesini +1 more
TL;DR: Wang et al. as mentioned in this paper proposed a dictionary learning model by employing patches based sparsity in order to denoise such Poisson phaseless measurements, which consists of three terms: (i) a representation term by an orthogonal dictionary, (ii) an L0 pseudo norm of the coefficient matrix, and (iii) a Kullback-Leibler divergence term to fit Phaseless Poisson data.
Posted Content
Overlapping Domain Decomposition Methods for Ptychographic Imaging
TL;DR: The overlapping Domain Decomposition Methods (DDMs) are proposed to solve the nonconvex optimization problem in ptychographic imaging, that decouple the problem defined on the whole domain into subproblems onlydefined on the subdomains with synchronizing information on the interface of these subDomains, thus leading to highly parallel algorithms with good load balance.