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Blind Image Deblurring Using Weighted Sum of Gaussian Kernels for Point Spread Function Estimation Hong LIU BenYong LIU Publication 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 Keyword: point spread function, blind deblurring, Gaussian kernel, total variation, Full Text: PDF(391.3KB)>>
Summary: 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. | ||||||||||
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