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Proceedings Paper

Context-based denoising of images using iterative wavelet thresholding
Author(s): Detlev Marpe; Hans L. Cycon; Gunther Zander; Kai-Uwe Barthel
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Paper Abstract

In this paper, we propose a spatially adaptive wavelet thresholding method using a context model that has been inspired by our prior work on image coding. The proposed context model relies on an estimation of the weighted variance in a local window of scale and space. Appropriately chosen weights are used to model the predominant correlations for a reliable statistical estimation. By iterating the context-based thresholding operation, a more accurate reconstruction can be achieved. Experimental results show that our proposed method yields significantly improved visual quality as well as lower mean squared error compared to the best recently published results in the denoising literature.

Paper Details

Date Published: 4 January 2002
PDF: 8 pages
Proc. SPIE 4671, Visual Communications and Image Processing 2002, (4 January 2002); doi: 10.1117/12.453135
Show Author Affiliations
Detlev Marpe, Heinrich-Hertz-Institut fuer Nachrichtentechnik (Germany)
Hans L. Cycon, Fachhochschule fuer Technik und Wirtschaft Berlin (Germany)
Gunther Zander, Fachhochschule fuer Technik und Wirtschaft Berlin (Germany)
Kai-Uwe Barthel, Fachhochschule fuer Technik und Wirtschaft Berlin (Germany)

Published in SPIE Proceedings Vol. 4671:
Visual Communications and Image Processing 2002
C.-C. Jay Kuo, Editor(s)

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