Mixed memory model for image processing and modeling with complex Daubechies wavelets

Diego Clonda; Jean-Marc Lina; Bernard Goulard

doi:10.1117/12.408659

4 December 2000 Mixed memory model for image processing and modeling with complex Daubechies wavelets

Diego Clonda, Jean-Marc Lina, Bernard Goulard

Proceedings Volume 4119, Wavelet Applications in Signal and Image Processing VIII; (2000) https://doi.org/10.1117/12.408659
Event: International Symposium on Optical Science and Technology, 2000, San Diego, CA, United States

Abstract

In this paper, we propose a statistical modeling of images based on a decomposition with complex-valued Daubechies wavelets. These wavelets possess interesting properties that can be turned into account in the modeling to obtain a better characterization of the images. This characterization is achieved by statistically modeling the wavelet coefficient distribution via hidden Markov tree model. The wavelet coefficients in an image are organized into three tree structures and this type of model has already been used successfully in this context by independently modeling each of these trees. We propose a further refinement by considering the joint modeling of the three trees with a so- called mixed memory hidden Markov tree model. The mode is base don a memory mixture, a general approach to obtain an approximation of the joint distribution in the presence of factorial Markov models. The utilization of such model s is quite general and can be applied to various signal- processing problems. To illustrate the interest of this model as well as the relevance of using complex Daubechies wavelets, we evaluate their performance for a classification and a denoising application.

Citation Download Citation

Diego Clonda, Jean-Marc Lina, and Bernard Goulard "Mixed memory model for image processing and modeling with complex Daubechies wavelets", Proc. SPIE 4119, Wavelet Applications in Signal and Image Processing VIII, (4 December 2000); https://doi.org/10.1117/12.408659

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