A variational formulation for physical noised image segmentation

Image segmentation is a hot topic in image science. In this paper we present a new variational segmentation model based on the theory of Mumford-Shah model. The aim of our model is to divide noised image, according to a certain criterion, into homogeneous and smooth regions that should correspond to...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Applied Mathematics-A Journal of Chinese Universities Ročník 30; číslo 1; s. 77 - 92
Hlavní autoři: Lou, Qiong, Peng, Jia-lin, Kong, De-xing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Heidelberg Editorial Committee of Applied Mathematics - A Journal of Chinese Universities 01.03.2015
Témata:
Applications of Mathematics
Mathematics
Mathematics and Statistics
68U10
49M30
non-Gaussian noise
image segmentation
variational method
primal-dual hybrid gradient algorithm
65K10
image denoising
ISSN:1005-1031, 1993-0445
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Image segmentation is a hot topic in image science. In this paper we present a new variational segmentation model based on the theory of Mumford-Shah model. The aim of our model is to divide noised image, according to a certain criterion, into homogeneous and smooth regions that should correspond to structural units in the scene or objects of interest. The proposed region-based model uses total variation as a regularization term, and different fidelity term can be used for image segmentation in the cases of physical noise, such as Gaussian, Poisson and multiplicative speckle noise. Our model consists of five weighted terms, two of them are responsible for image denoising based on fidelity term and total variation term, the others assure that the three conditions of adherence to the data, smoothing, and discontinuity detection are met at once. We also develop a primal-dual hybrid gradient algorithm for our model. Numerical results on various synthetic and real images are provided to compare our method with others, these results show that our proposed model and algorithms are effective.
ISSN:1005-1031
1993-0445
DOI:10.1007/s11766-015-3217-7