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A Second-Order Regularization Method of Object Reconstruction in Hydrodynamic Experiments

Authors:
Linghai Kong
Haibo Xu

Keywords: Regularization method; Mixed Laplace-Gaussian Noise; ALM; EM; Image Reconstruction

Abstract:
A new higher-order regularization model is investigated under the assumption of mixed Laplace-Gaussian noise, which plays an important role in tomography reconstruction and quantitative analysis of hydrodynamic experiments. To solve the model numerically, adaptive stopping functions are introduced to improve the classical augmented Lagrangian method, and an adaptive soft-shrinking formula is derived. To acquire efficiency and reliability, it is further combined with a variant of the expectation maximization method. Some experimental tests are performed for image denoising and object reconstruction.

Pages: 1 to 6

Copyright: Copyright (c) IARIA, 2017

Publication date: November 12, 2017

Published in: conference

ISSN: 2308-4499

ISBN: 978-1-61208-599-9

Location: Barcelona, Spain

Dates: from November 12, 2017 to November 16, 2017