Spatially adaptive wavelet denoising using the minimum description length principle.

Jiecheng Xie; Dali Zhang; Wenli Xu

doi:10.1109/tip.2004.823828

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Spatially adaptive wavelet denoising using the minimum description length principle.

Jiecheng Xie·Dali Zhang·Wenli Xu

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Abstract

This paper presents a new spatially adaptive wavelet denoising method. Based on a doubly stochastic process model of wavelet coefficients, the method gives a new threshold, which varies spatially according to the variances of the coefficients, using the minimum description length (MDL) principle. The new threshold is not only easier to analyze since it is in a closed form, but also provides more facility for future compression than several other methods, almost without deteriorating mean square error risk.

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Algorithms Computer Simulation Image Enhancement Image Interpretation, Computer-Assisted Information Storage and Retrieval Models, Statistical Reproducibility of Results Sensitivity and Specificity Signal Processing, Computer-Assisted Stochastic Processes Comparative Study Evaluation Study Journal Article Research Support, Non-U.S. Gov't Validation Study

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Jiecheng Xie, Dali Zhang, Wenli Xu. 2004. Spatially adaptive wavelet denoising using the minimum description length principle.. https://doi.org/10.1109/tip.2004.823828

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