Minimum description length denoising with histogram models

被引：6

作者：

Kumar, Vibhor ^{[1
]}

Heikkonen, Jukka ^{[1
]}

Rissanen, Jorma ^{[1
]}

Kaski, Kimmo ^{[1
]}

机构：

[1] Helsinki Univ Technol, Lab Computat Engn, FIN-02015 Helsinki, Finland

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2006年 / 54卷 / 08期

关键词：

complexity; denoising; minimum description length; wavelets;

D O I：

10.1109/TSP.2006.877635

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we relax the usual assumptions in denoising that the data consist of a "true" signal to which normally distributed noise is added. Instead of regarding noise as the high-frequency part in the data to be removed either by a "hard" or "soft" threshold, we define it as that part in the data which is harder to compress than the rest with the class of models considered. Here, we model the data by two histograms: one for the denoised signal and the other for the noise, both represented by wavelet coefficients. A code length can be calculated for each part, and by the principle of minimum description length the optimal decomposition results by minimization of the sum of the two code lengths.

引用

页码：2922 / 2928

页数：7

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