A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image processing and data analysis: the multiscale approach
Image processing and data analysis: the multiscale approach
A tree of median pyramidal decompositions with an application to signal denoising
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 01
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
Block-median pyramidal transform: analysis and denoisingapplications
IEEE Transactions on Signal Processing
Spatially adaptive wavelet thresholding with context modeling for image denoising
IEEE Transactions on Image Processing
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We consider a median pyramidal transform for denoising applications. Traditional techniques of pyramidal denoising are similar to those in wavelet-based methods. In order to remove noise, they use the thresholding of transform coefficients. We propose to model the structure of the transform coefficients as a Markov random field. The goal of modeling transform coefficients is to retain significant coefficients on each scale and to discard the rest. Estimation of the transform coefficient structure is obtained via a Markov chain sampler. A technique is proposed to estimate the parameters of the field's distribution. The advantage of our method is that we are able to utilize the interactions between transform coefficients, both within each scale and among the scales, which leads to denoising improvement as demonstrated by numerical simulations.