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Zhu, Daan, Razaz, Moe, Hemmings, Andrew 
ORCID: https://orcid.org/0000-0003-3053-3134 and Wang, Binhai
(2004)
Multi-PSF modelling for X-ray diffraction pattern reconstruction.
In: 12th European Signal Processing Conference, 2004-09-06 - 2004-09-10.
Abstract
In this paper, we present a point spread function (PSF) modelling technique to improve restoration of x-ray diffraction pattern (XRD). Different diffraction areas have different distortion orientations due to diffuse light distortion (DLD). A new multiple PSF model is introduced and used to restore XRD data. Raw PSFs are collected from isolated spots from x-ray diffraction pattern in high resolution areas which represent orientation of DLDs. An adaptive ridge regression (ARR) technique is used to remove noise from the raw PSF. A target Gaussian function is used to model the raw PSFs. A gradient descent algorithm (GDA) is used to find optimum parameters in a Gaussian function. A set of XRD data are restored by an iterative deconvolution algorithm (IDA) using the modelled PSFs. Experimental results using a single and multiple PSFs are presented and discussed. We show that by using a multiple PSF model in the deconvolution algorithm improved restored X-ray patterns are obtained and as a result the symmetry estimator and χ2 are improved.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences Faculty of Science > School of Biological Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Biophysical Chemistry (former - to 2017) Faculty of Science > Research Groups > Molecular Microbiology Faculty of Science > Research Groups > Plant Sciences Faculty of Science > Research Groups > Chemistry of Life Processes Faculty of Science > Research Centres > Centre for Molecular and Structural Biochemistry |
| Related URLs: | |
| Depositing User: | EPrints Services |
| Date Deposited: | 01 Oct 2010 13:40 |
| Last Modified: | 07 Mar 2023 12:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/1822 |
| DOI: |
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