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Sensors 2017, 17(1), 174; doi:10.3390/s17010174

Effective Alternating Direction Optimization Methods for Sparsity-Constrained Blind Image Deblurring

Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China
Department of Business and Computer Science, Southwestern Oklahoma State University, Oklahoma, OK 73096, USA
School of Computer, Wuhan University, Wuhan 430072, China
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
Authors to whom correspondence should be addressed.
Academic Editor: Muhammad Imran
Received: 1 November 2016 / Revised: 4 January 2017 / Accepted: 4 January 2017 / Published: 18 January 2017
(This article belongs to the Special Issue Topology Control in Emerging Sensor Networks)
View Full-Text   |   Download PDF [8441 KB, uploaded 18 January 2017]   |  


Single-image blind deblurring for imaging sensors in the Internet of Things (IoT) is a challenging ill-conditioned inverse problem, which requires regularization techniques to stabilize the image restoration process. The purpose is to recover the underlying blur kernel and latent sharp image from only one blurred image. Under many degraded imaging conditions, the blur kernel could be considered not only spatially sparse, but also piecewise smooth with the support of a continuous curve. By taking advantage of the hybrid sparse properties of the blur kernel, a hybrid regularization method is proposed in this paper to robustly and accurately estimate the blur kernel. The effectiveness of the proposed blur kernel estimation method is enhanced by incorporating both the L 1 -norm of kernel intensity and the squared L 2 -norm of the intensity derivative. Once the accurate estimation of the blur kernel is obtained, the original blind deblurring can be simplified to the direct deconvolution of blurred images. To guarantee robust non-blind deconvolution, a variational image restoration model is presented based on the L 1 -norm data-fidelity term and the total generalized variation (TGV) regularizer of second-order. All non-smooth optimization problems related to blur kernel estimation and non-blind deconvolution are effectively handled by using the alternating direction method of multipliers (ADMM)-based numerical methods. Comprehensive experiments on both synthetic and realistic datasets have been implemented to compare the proposed method with several state-of-the-art methods. The experimental comparisons have illustrated the satisfactory imaging performance of the proposed method in terms of quantitative and qualitative evaluations. View Full-Text
Keywords: imaging sensors; blind deblurring; image restoration; total variation; total generalized variation; alternating direction method of multipliers imaging sensors; blind deblurring; image restoration; total variation; total generalized variation; alternating direction method of multipliers

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Xiong, N.; Liu, R.W.; Liang, M.; Wu, D.; Liu, Z.; Wu, H. Effective Alternating Direction Optimization Methods for Sparsity-Constrained Blind Image Deblurring. Sensors 2017, 17, 174.

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