MULTI-PSF MODELLING FOR X-RAY DIFFRACTION PATTERN RECONSTRUCTION (WedAmOR1)
Author(s) :
Daan Zhu (CMP, University of East Anglia, England)
Moe Razaz (CMP, University of East Anglia, England)
Binhai Wang (CMP, University of East Anglia, England)
Andrew Hemmnings (BIO, University of East Anglia, England)
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 $\chiÓ2$ are improved.

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