Image deconvolution using hidden Markov tree modeling of complex wavelet packets

Image deconvolution using hidden Markov tree modeling of complex wavelet packets

In this paper, we propose to use a hidden Markov tree modeling of the complex wavelet packet transform, to capture the inter-scale dependencies of natural images. First, the observed image, blurred and noisy, is deconvolved without regularization. Then its transform is denoised within a Bayesian fra...

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Bibliographische Detailangaben
Hauptverfasser: Jalobeanu, A., Kingsbury, N., Zerubia, J.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Bayesian methods
Deconvolution
Filtering
Frequency
Hidden Markov models
Noise reduction
Signal processing
Signal processing algorithms
Wavelet packets
Wavelet transforms
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Beschreibung
Zusammenfassung:In this paper, we propose to use a hidden Markov tree modeling of the complex wavelet packet transform, to capture the inter-scale dependencies of natural images. First, the observed image, blurred and noisy, is deconvolved without regularization. Then its transform is denoised within a Bayesian framework using the proposed model, whose parameters are estimated by an EM technique. The total complexity of this new deblurring algorithm remains O(N).
DOI:10.1109/ICIP.2001.958988