Image deblurring using a perturbation-basec regularization approach
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
KAUST Grant NumberOSR-2016-KKI-2899
Online Publication Date2017-11-02
Print Publication Date2017-08
Permanent link to this recordhttp://hdl.handle.net/10754/626255
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AbstractThe image restoration problem deals with images in which information has been degraded by blur or noise. In this work, we present a new method for image deblurring by solving a regularized linear least-squares problem. In the proposed method, a synthetic perturbation matrix with a bounded norm is forced into the discrete ill-conditioned model matrix. This perturbation is added to enhance the singular-value structure of the matrix and hence to provide an improved solution. A method is proposed to find a near-optimal value of the regularization parameter for the proposed approach. To reduce the computational complexity, we present a technique based on the bootstrapping method to estimate the regularization parameter for both low and high-resolution images. Experimental results on the image deblurring problem are presented. Comparisons are made with three benchmark methods and the results demonstrate that the proposed method clearly outperforms the other methods in terms of both the output PSNR and SSIM values.
CitationAlanazi AM, Ballal T, Masood M, Al-Naffouri TY (2017) Image deblurring using a perturbation-based regularization approach. 2017 25th European Signal Processing Conference (EUSIPCO). Available: http://dx.doi.org/10.23919/eusipco.2017.8081637.
SponsorsThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR- 2016-KKI-2899.