Title :
Multi-PSF modelling for x-ray diffraction pattern reconstruction
Author :
Daan Zhu ; Razaz, Moe ; Hemmnings, Andrew ; Binhai Wang
Author_Institution :
Sch. of Comput. Sci., Univ. of East Anglia, Norwich, UK
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.
Keywords :
Gaussian processes; X-ray diffraction; deconvolution; gradient methods; iterative methods; optical distortion; optical transfer function; signal denoising; signal resolution; signal restoration; ARR technique; DLD; GOA; Gaussian function; IDA; X-ray diffraction pattern reconstruction; XRD data restoration; adaptive ridge regression technique; diffuse light distortion; distortion orientations; gradient descent algorithm; iterative deconvolution algorithm; multiPSF modelling; noise removal; point spread function modelling technique; symmetry estimator; Abstracts; Algorithm design and analysis; Artificial intelligence; Crystals; Image restoration; Optimization;
Conference_Titel :
Signal Processing Conference, 2004 12th European
Conference_Location :
Vienna
Print_ISBN :
978-320-0001-65-7