Denoised Wigner distribution deconvolution via low-rank matrix completion
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Health Sciences and Technology
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Mechanical Engineering; Singapore-MIT Alliance for Research and Technology (SMART) Centre (Singapore)
Wigner distribution deconvolution (WDD) is a decades-old method for recovering phase from intensity measurements. Although the technique offers an elegant linear solution to the quadratic phase retrieval problem, it has seen limited adoption due to its high computational/memory requirements and the fact that the technique often exhibits high noise sensitivity. Here, we propose a method for noise suppression in WDD via low-rank noisy matrix completion. Our technique exploits the redundancy of an object’s phase space to denoise its WDD reconstruction. We show in model calculations that our technique outperforms other WDD algorithms as well as modern iterative methods for phase retrieval such as ptychography. Here, our results suggest that a class of phase retrieval techniques relying on regularized direct inversion of ptychographic datasets (instead of iterative reconstruction techniques) can provide accurate quantitative phase information in the presence of high levels of noise.
- Research Organization:
- Krell Institute, Ames, IA (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- FG02-97ER25308
- OSTI ID:
- 1434528
- Journal Information:
- Optics Express, Vol. 24, Issue 18; ISSN 1094-4087
- Publisher:
- Optical Society of America (OSA)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Electron ptychography of 2D materials to deep sub-ångström resolution
|
journal | July 2018 |
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