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Image analysis using a dual-tree M-band wavelet transform.

Caroline Chaux·Laurent Duval·Jean-Christophe Pesquet
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Abstract

We propose a two-dimensional generalization to the M-band case of the dual-tree decomposition structure (initially proposed by Kingsbury and further investigated by Selesnick) based on a Hilbert pair of wavelets. We particularly address: 1) the construction of the dual basis and 2) the resulting directional analysis. We also revisit the necessary pre-processing stage in the M-band case. While several reconstructions are possible because of the redundancy of the representation, we propose a new optimal signal reconstruction technique, which minimizes potential estimation errors. The effectiveness of the proposed M-band decomposition is demonstrated via denoising comparisons on several image types (natural, texture, seismics), with various M-band wavelets and thresholding strategies. Significant improvements in terms of both overall noise reduction and direction preservation are observed.

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AlgorithmsArtificial IntelligenceImage EnhancementImage Interpretation, Computer-AssistedInformation Storage and RetrievalNumerical Analysis, Computer-AssistedPattern Recognition, AutomatedSignal Processing, Computer-AssistedEvaluation StudyJournal Article

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Caroline Chaux, Laurent Duval, Jean-Christophe Pesquet. 2006. Image analysis using a dual-tree M-band wavelet transform.. https://doi.org/10.1109/tip.2006.875178

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