Using Gaussian mixture model to fix errors in SFS approach based on propagation Online publication date: Tue, 27-Aug-2019
by Wenmin Huang; Jiquan Ma
International Journal of Computational Science and Engineering (IJCSE), Vol. 19, No. 4, 2019
Abstract: A new Gaussian mixture model is used to improve the quality of propagation method for SFS in this paper. The improved algorithm can overcome most difficulties of propagation SFS method including slow convergence, interdependence of propagation nodes and error accumulation. For slow convergence and interdependence of propagation nodes, stable propagation source and integration path are used to make sure that the reconstruction work of each pixel in the image is independent. A Gaussian mixture model based on prior conditions has been proposed to fix the error of integration. Good result has achieved in the experiment for Lambert composite image of front illumination.
Online publication date: Tue, 27-Aug-2019
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