Blind Image Deblurring Using Weighted Sum of Gaussian Kernels for Point Spread Function Estimation

Hong LIU  BenYong LIU  

IEICE TRANSACTIONS on Information and Systems   Vol.E98-D   No.11   pp.2026-2029
Publication Date: 2015/11/01
Publicized: 2015/08/05
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2015EDL8121
Type of Manuscript: LETTER
Category: Image Processing and Video Processing
point spread function,  blind deblurring,  Gaussian kernel,  total variation,  

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Point spread function (PSF) estimation plays a paramount role in image deblurring processing, and traditionally it is solved by parameter estimation of a certain preassumed PSF shape model. In real life, the PSF shape is generally arbitrary and complicated, and thus it is assumed in this manuscript that a PSF may be decomposed as a weighted sum of a certain number of Gaussian kernels, with weight coefficients estimated in an alternating manner, and an l1 norm-based total variation (TVl1) algorithm is adopted to recover the latent image. Experiments show that the proposed method can achieve satisfactory performance on synthetic and realistic blurred images.