Using automatic differentiation as a general framework for ptychographic reconstruction
Abstract
Coherent diffraction imaging methods enable imaging beyond lens-imposed resolution limits. In these methods, the object can be recovered by minimizing an error metric that quantifies the difference between diffraction patterns as observed, and those calculated from a present guess of the object. Efficient minimization methods require analytical calculation of the derivatives of the error metric, which is not always straightforward. This limits our ability to explore variations of basic imaging approaches. Here, we propose to substitute analytical derivative expressions with the automatic differentiation method, whereby we can achieve object reconstruction by specifying only the physics-based experimental forward model. We demonstrate the generality of the proposed method through straightforward object reconstruction for a variety of complex ptychographic experimental models.
- Publication Date:
- Research Org.:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division; National Institutes of Health (NIH); USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division
- OSTI Identifier:
- 1527070
- Alternate Identifier(s):
- OSTI ID: 1543288
- Grant/Contract Number:
- AC02-06CH11357
- Resource Type:
- Published Article
- Journal Name:
- Optics Express
- Additional Journal Information:
- Journal Name: Optics Express Journal Volume: 27 Journal Issue: 13; Journal ID: ISSN 1094-4087
- Publisher:
- Optical Society of America
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 47 OTHER INSTRUMENTATION
Citation Formats
Kandel, Saugat, Maddali, S., Allain, Marc, Hruszkewycz, Stephan O., Jacobsen, Chris, and Nashed, Youssef S. G. Using automatic differentiation as a general framework for ptychographic reconstruction. United States: N. p., 2019.
Web. doi:10.1364/OE.27.018653.
Kandel, Saugat, Maddali, S., Allain, Marc, Hruszkewycz, Stephan O., Jacobsen, Chris, & Nashed, Youssef S. G. Using automatic differentiation as a general framework for ptychographic reconstruction. United States. https://doi.org/10.1364/OE.27.018653
Kandel, Saugat, Maddali, S., Allain, Marc, Hruszkewycz, Stephan O., Jacobsen, Chris, and Nashed, Youssef S. G. Wed .
"Using automatic differentiation as a general framework for ptychographic reconstruction". United States. https://doi.org/10.1364/OE.27.018653.
@article{osti_1527070,
title = {Using automatic differentiation as a general framework for ptychographic reconstruction},
author = {Kandel, Saugat and Maddali, S. and Allain, Marc and Hruszkewycz, Stephan O. and Jacobsen, Chris and Nashed, Youssef S. G.},
abstractNote = {Coherent diffraction imaging methods enable imaging beyond lens-imposed resolution limits. In these methods, the object can be recovered by minimizing an error metric that quantifies the difference between diffraction patterns as observed, and those calculated from a present guess of the object. Efficient minimization methods require analytical calculation of the derivatives of the error metric, which is not always straightforward. This limits our ability to explore variations of basic imaging approaches. Here, we propose to substitute analytical derivative expressions with the automatic differentiation method, whereby we can achieve object reconstruction by specifying only the physics-based experimental forward model. We demonstrate the generality of the proposed method through straightforward object reconstruction for a variety of complex ptychographic experimental models.},
doi = {10.1364/OE.27.018653},
journal = {Optics Express},
number = 13,
volume = 27,
place = {United States},
year = {Wed Jun 19 00:00:00 EDT 2019},
month = {Wed Jun 19 00:00:00 EDT 2019}
}
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https://doi.org/10.1364/OE.27.018653
https://doi.org/10.1364/OE.27.018653
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Cited by: 24 works
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Figures / Tables:
Table 1: Schema for reverse-mode automatic differentiation. The gradient is evaluated through an accumulation of intermediate values calculated for the individual elementary functions and their derivatives.
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