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Sensors 2018, 18(4), 1175; https://doi.org/10.3390/s18041175

A Convex Constraint Variational Method for Restoring Blurred Images in the Presence of Alpha-Stable Noises

1,2
,
1,2
and
1,2,*
1
National Engineering Research Center of Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2
Key Lab of Broadband Wireless Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Ministry of Education, Nanjing 210003, China
*
Author to whom correspondence should be addressed.
Received: 12 February 2018 / Revised: 6 April 2018 / Accepted: 10 April 2018 / Published: 12 April 2018
(This article belongs to the Special Issue Software-Defined Networking Based Mobile Networks)
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Abstract

Blurred image restoration poses a great challenge under the non-Gaussian noise environments in various communication systems. In order to restore images from blur and alpha-stable noise while also preserving their edges, this paper proposes a variational method to restore the blurred images with alpha-stable noises based on the property of the meridian distribution and the total variation (TV). Since the variational model is non-convex, it cannot guarantee a global optimal solution. To overcome this drawback, we also incorporate an additional penalty term into the deblurring and denoising model and propose a strictly convex variational method. Due to the convexity of our model, the primal-dual algorithm is adopted to solve this convex variational problem. Our simulation results validate the proposed method. View Full-Text
Keywords: image deblurring; variational method; alpha-stable noise; primal-dual algorithm; total variational image deblurring; variational method; alpha-stable noise; primal-dual algorithm; total variational
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Yang, Z.; Yang, Z.; Gui, G. A Convex Constraint Variational Method for Restoring Blurred Images in the Presence of Alpha-Stable Noises. Sensors 2018, 18, 1175.

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