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Title: Spatially dependent regularization parameter selection for total generalized variation-based image denoising

Abstract

We propose a novel image denoising model based on the total generalized variation (TGV) regularization. In the model, a spatially dependent regularization parameter is utilized to adaptively fit the local image features, resulting in further exploitation of the denoising potential of the TGV regularization. The proposed model is formulated under a joint optimization framework, by which the estimations of the restored image and the regularization parameter are achieved simultaneously. Furthermore, the model is general purpose that can handle various types of noise occurring in image processing. An alternating minimization-based numerical scheme is especially developed, which leads to an efficient algorithmic solution to the nonconvex optimization problem. Numerical experiments are reported to illustrate the effectiveness of our model in terms of both peak signal-to-noise ratio and visual perception.

Authors:
; ;  [1]
  1. University of Electronic Science and Technology of China, School of Mathematical Sciences/Research Center for Image and Vision Computing (China)
Publication Date:
OSTI Identifier:
22769387
Resource Type:
Journal Article
Journal Name:
Computational and Applied Mathematics
Additional Journal Information:
Journal Volume: 37; Journal Issue: 1; Other Information: Copyright (c) 2018 SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0101-8205
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICAL METHODS AND COMPUTING; IMAGE PROCESSING; MATHEMATICAL SOLUTIONS; MINIMIZATION; SIGNAL-TO-NOISE RATIO

Citation Formats

Ma, Tian-Hui, Huang, Ting-Zhu, and Zhao, Xi-Le. Spatially dependent regularization parameter selection for total generalized variation-based image denoising. United States: N. p., 2018. Web. doi:10.1007/S40314-016-0342-8.
Ma, Tian-Hui, Huang, Ting-Zhu, & Zhao, Xi-Le. Spatially dependent regularization parameter selection for total generalized variation-based image denoising. United States. doi:10.1007/S40314-016-0342-8.
Ma, Tian-Hui, Huang, Ting-Zhu, and Zhao, Xi-Le. Thu . "Spatially dependent regularization parameter selection for total generalized variation-based image denoising". United States. doi:10.1007/S40314-016-0342-8.
@article{osti_22769387,
title = {Spatially dependent regularization parameter selection for total generalized variation-based image denoising},
author = {Ma, Tian-Hui and Huang, Ting-Zhu and Zhao, Xi-Le},
abstractNote = {We propose a novel image denoising model based on the total generalized variation (TGV) regularization. In the model, a spatially dependent regularization parameter is utilized to adaptively fit the local image features, resulting in further exploitation of the denoising potential of the TGV regularization. The proposed model is formulated under a joint optimization framework, by which the estimations of the restored image and the regularization parameter are achieved simultaneously. Furthermore, the model is general purpose that can handle various types of noise occurring in image processing. An alternating minimization-based numerical scheme is especially developed, which leads to an efficient algorithmic solution to the nonconvex optimization problem. Numerical experiments are reported to illustrate the effectiveness of our model in terms of both peak signal-to-noise ratio and visual perception.},
doi = {10.1007/S40314-016-0342-8},
journal = {Computational and Applied Mathematics},
issn = {0101-8205},
number = 1,
volume = 37,
place = {United States},
year = {2018},
month = {3}
}