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Proceedings Paper

Motion deblurring with graph Laplacian regularization
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Paper Abstract

In this paper, we develop a regularization framework for image deblurring based on a new definition of the normalized graph Laplacian. We apply a fast scaling algorithm to the kernel similarity matrix to derive the symmetric, doubly stochastic filtering matrix from which the normalized Laplacian matrix is built. We use this new definition of the Laplacian to construct a cost function consisting of data fidelity and regularization terms to solve the ill-posed motion deblurring problem. The final estimate is obtained by minimizing the resulting cost function in an iterative manner. Furthermore, the spectral properties of the Laplacian matrix equip us with the required tools for spectral analysis of the proposed method. We verify the effectiveness of our iterative algorithm via synthetic and real examples.

Paper Details

Date Published: 27 February 2015
PDF: 8 pages
Proc. SPIE 9404, Digital Photography XI, 94040C (27 February 2015); doi: 10.1117/12.2084585
Show Author Affiliations
Amin Kheradmand, Univ. of California, Santa Cruz (United States)
Peyman Milanfar, Univ. of California, Santa Cruz (United States)

Published in SPIE Proceedings Vol. 9404:
Digital Photography XI
Nitin Sampat; Radka Tezaur; Dietmar Wüller, Editor(s)

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