Image restoration: The wavelet-based approach

被引：1
|
作者：
Ndjountche, T [1 ]
Unbehauen, R [1 ]
机构：
[1] Univ Erlangen Nurnberg, Dept Elect Elect & Informat Engn, D-91058 Erlangen, Germany
来源：
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | 2003年 / 17卷 / 01期
关键词：
wavelet transform; thresholding; hidden Markov model; expectation-maximization algorithm; noise reduction;
D O I：
10.1142/S0218001403002277
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Wavelet-based techniques are suitable for recovering a signal corrupted by noise. The time- and frequency-localization capabilities of wavelets provide better noise reduction and less signal distortion than conventional filtering methods. The noise reduction technique used in this paper is based on the hidden Markov model (HMM) structure, which can efficiently shape the statistical characteristics of practical data. As confirmed by numerical results, the HMM based approach provides a significant performance improvement over competing methods.
引用
收藏
页码：151 / 162
页数：12
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