Multi-PSF modelling for X-ray diffraction pattern reconstruction

Daan Zhu (Lead Author), Moe Razaz, Andrew Hemmings, Binhai Wang

Research output: Contribution to conferencePaperpeer-review


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.
Original languageEnglish
Number of pages4
Publication statusPublished - 2004
Event12th European Signal Processing Conference - Vienna, Austria
Duration: 6 Sep 200410 Sep 2004


Conference12th European Signal Processing Conference

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