Can I read Maximum Likelihood based Phase-Retrieval using Fresnel Propagation Forward Models with Optional Constraints on EtoBox?

Maximum Likelihood based Phase-Retrieval using Fresnel Propagation Forward Models with Optional Constraints by Mohan, K. Aditya; Forien, Jean-Baptiste; Sridhar, Venkatesh; Cuadra, Jefferson A.; Parkinson, Dilworth is a scholarly article available to read on EtoBox.

What is Maximum Likelihood based Phase-Retrieval using Fresnel Propagation Forward Models with Optional Constraints about?

X-ray phase-contrast tomography (XPCT) is widely used for high contrast 3D imaging using either synchrotron or laboratory microfocus X-ray sources. XPCT enables an order of magnitude improvement in image contrast of the reconstructed material interfaces with low X-ray absorption contrast. The dominant approaches to 3D reconstruction using XPCT relies on the use of phase-retrieval algorithms that make one or more limiting approximations for the experimental configuration and material properties. Since many experimental scenarios violate such approximations, the resulting reconstructions contain blur, artifacts, or other quantitative inaccuracies. Our solution to this problem is to formulate new iterative non-linear phase-retrieval (NLPR) algorithms that avoid such limiting approximations. Compared to the widely used state-of-the-art approaches, we show that our proposed algorithms result in sharp and quantitatively accurate reconstruction with reduced artifacts. Unlike existing NLPR algorithms, our approaches avoid the laborious manual tuning of regularization hyper-parameters while still achieving the stated goals. As an alternative to regularization, we propose explicit constraint

Author

Mohan, K. Aditya; Forien, Jean-Baptiste; Sridhar, Venkatesh; Cuadra, Jefferson A.; Parkinson, Dilworth

Published

2023

Language

EN