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Proceedings Paper

Best wavelet packet basis for joint image deblurring-denoising and compression
Author(s): Pierre Dherete; Sylvain Durand; Jacques Froment; Bernard Rouge
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Paper Abstract

We propose a unique mathematical framework to deblur, denoise and compress natural images. Images are decomposed in a wavelet packet basis adapted both to the deblurring filter and to the denoising process. Effective denoising is performed by thresholding small wavelet packet coefficients while deblurring is obtained by multiplying the coefficients with a deconvolution kernel. This representation is compressed by quantizing the remaining coefficients and by coding the values using a context-based entropy coder. We present examples of such treatments on a satellite image chain. The results show a significant improvement compared to separate treatments with up-to-date compression approach.

Paper Details

Date Published: 30 January 2003
PDF: 12 pages
Proc. SPIE 4793, Mathematics of Data/Image Coding, Compression, and Encryption V, with Applications, (30 January 2003); doi: 10.1117/12.455786
Show Author Affiliations
Pierre Dherete, Thales (France)
Sylvain Durand, Univ. de Picardie Jules Verne (France)
Ecole Normale Superieure de Cachan (France)
Jacques Froment, Univ. Rene Descartes (France)
Ecole Normale Superieure de Cachan (France)
Bernard Rouge, Ctr. National d'Etudes Spatiales (France)
Ecole Normale Superieure de Cachan (France)


Published in SPIE Proceedings Vol. 4793:
Mathematics of Data/Image Coding, Compression, and Encryption V, with Applications
Mark S. Schmalz, Editor(s)

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