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Proceedings Paper

Analysis of wavelet image denoising model in Besov spaces
Author(s): Qibin Fan; Minkai Jiang; Wenping Xiao
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Paper Abstract

In this paper, we discuss the image denoising model which DeVore et al. had established, in which both distance and smoothness can be measured by the objective function, and analysis the model for wavelet image denoising in the Besov spaces with p = q. In addition, we give the exact thresholds for the model, and prove that for 0 < p <1 the effect of noise removal using our methods is in between hard wavelet shrinkage and soft wavelet shrinkage. For the case 0 < p < 1 and 1 ≤ p ≤ ∞, which refers to the problems on the convergence of the iteration of the equations and on the complexity of computation, we give the simplified algorithms. Comparing the threshold given by this paper with Lorenz threshold, we conclude that the former is more meticulous than the latter for the model.

Paper Details

Date Published: 15 November 2007
PDF: 9 pages
Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67862P (15 November 2007); doi: 10.1117/12.752714
Show Author Affiliations
Qibin Fan, Wuhan Univ. (China)
Minkai Jiang, Wuhan Univ. (China)
Wenping Xiao, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 6786:
MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition

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