NDLI: Image decomposition combining a total variational filter and a Tikhonov quadratic filter

Content Provider	Springer Nature Link
Author	Hao, Yan Xu, Jianlou Bai, Jian Han, Yu
Copyright Year	2013
Abstract	In this paper, we propose a new variational model for image decomposition which separates an image into a cartoon, consisting only of geometric objects and an oscillatory component, consisting of texture or noise. In the new model, the $$\hbox {H}^{-1}$$ -norm is considered as the data fitting term and the regularization term is composed of a total variational filter and a Tikhonov quadratic filter. These two filters can be automatically selected by a soft threshold function. When the pixels belong to the cartoon area, the total variational filter is adopted, which can preserve the geometric structures of image such as the edges, well. When the pixels belong to texture region, the Tikhonov quadratic filter is chosen,which can extract the texture of image well. To solve the proposed model effectively, the split Bregman method is employed. Experimental results demonstrate that the proposed model and algorithm can obtain better decomposition results than those of classical models.
Starting Page	739
Ending Page	751
Page Count	13
File Format	PDF
ISSN	09236082
Journal	Multidimensional Systems and Signal Processing
Volume Number	26
Issue Number	3
e-ISSN	15730824
Language	English
Publisher	Springer US
Publisher Date	2013-10-25
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Total variation Image decomposition Split Bregman Image denoising Circuits and Systems Electrical Engineering Signal, Image and Speech Processing Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Signal Processing Information Systems Computer Science Applications Software Hardware and Architecture

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