TY - JOUR
T1 - A Convex Variational Model for Restoring SAR Images Corrupted by Multiplicative Noise
AU - Yang, Hanmei
AU - Yang, Hanmei
AU - Li, Jiachang
AU - Shen, Lixin
AU - Lu, Jian
AU - Lu, Jian
N1 - Funding Information:
This paper studies a new convex variational model for denoising and deblurring images with multiplicative noise. Considering the statistical property of the multiplicative noise following Nakagami distribution, the denoising model consists of a data fidelity term, a quadratic penalty term, and a total variation regularization term. Here, the quadratic penalty term is mainly designed to guarantee the model to be strictly convex under a mild condition. Furthermore, the model is extended for the simultaneous denoising and deblurring case by introducing a blurring operator. We also study some mathematical properties of the proposed model. In addition, the model is solved by applying the primal-dual algorithm. The experimental results show that the proposed method is promising in restoring (blurred) images with multiplicative noise. Natural Science Foundation of Guangdong Province 2020B1515310008 Project of Educational Commission of Guangdong Province of China 2019KZDZX1007
Publisher Copyright:
© 2020 Hanmei Yang et al.
PY - 2020
Y1 - 2020
N2 - This paper studies a new convex variational model for denoising and deblurring images with multiplicative noise. Considering the statistical property of the multiplicative noise following Nakagami distribution, the denoising model consists of a data fidelity term, a quadratic penalty term, and a total variation regularization term. Here, the quadratic penalty term is mainly designed to guarantee the model to be strictly convex under a mild condition. Furthermore, the model is extended for the simultaneous denoising and deblurring case by introducing a blurring operator. We also study some mathematical properties of the proposed model. In addition, the model is solved by applying the primal-dual algorithm. The experimental results show that the proposed method is promising in restoring (blurred) images with multiplicative noise.
AB - This paper studies a new convex variational model for denoising and deblurring images with multiplicative noise. Considering the statistical property of the multiplicative noise following Nakagami distribution, the denoising model consists of a data fidelity term, a quadratic penalty term, and a total variation regularization term. Here, the quadratic penalty term is mainly designed to guarantee the model to be strictly convex under a mild condition. Furthermore, the model is extended for the simultaneous denoising and deblurring case by introducing a blurring operator. We also study some mathematical properties of the proposed model. In addition, the model is solved by applying the primal-dual algorithm. The experimental results show that the proposed method is promising in restoring (blurred) images with multiplicative noise.
UR - http://www.scopus.com/inward/record.url?scp=85087202747&partnerID=8YFLogxK
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U2 - 10.1155/2020/1952782
DO - 10.1155/2020/1952782
M3 - Article
AN - SCOPUS:85087202747
SN - 1024-123X
VL - 2020
JO - Mathematical Problems in Engineering
JF - Mathematical Problems in Engineering
M1 - 1952782
ER -