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Approximation BFGS methods for nonlinear image restoration

✍ Scribed by Lin-Zhang Lu; Michael K. Ng; Fu-Rong Lin

Publisher: Elsevier Science
Year: 2009
Tongue: English
Weight: 634 KB
Volume: 226
Category: Article
ISSN: 0377-0427
DOI: 10.1016/j.cam.2008.05.056

No coin nor oath required. For personal study only.

✦ Synopsis

We consider the iterative solution of unconstrained minimization problems arising from nonlinear image restoration. Our approach is based on a novel generalized BFGS method for such large-scale image restoration minimization problems. The complexity per step of the method is of O(n log n) operations and only O(n) memory allocations are required, where n is the number of image pixels. Based on the results given in [Carmine Di Fiore, Stefano Fanelli, Filomena Lepore, Paolo Zellini, Matrix algebras in quasi-Newton methods for unconstrained minimization, Numer. Math. 94 (2003) 479-500], we show that the method is globally convergent for our nonlinear image restoration problems. Experimental results are presented to illustrate the effectiveness of the proposed method.

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