摘要
Wavelet transformation and hidden Markov model are used in wavelet-based HMT model for analyzing andprocessing images. Expected Maximization(EM) algorithm used in training model results in slow convergence. Thepersistence, exponential decay characteristics of wavelet coefficient are analyzed. A model parameter initializationmethod is proposed. This method provides reasonable initial model value, reduces training time greatly. Its applica-tion in image de-noising demonstrates is validity.
Wavelet transformation and hidden Markov model are used in wavelet-based HMT model for analyzing and processing images. Expected Maximization (EM) algorithm used in training model results in slow convergence. The persistence, exponential decay characteristics of wavelet coefficient are analyzed. A model parameter initialization method is proposed. This method provides reasonable initial model value, reduces training time greatly. Its application in image de-noising demonstrates is validity.
出处
《计算机科学》
CSCD
北大核心
2003年第1期85-86,77,共3页
Computer Science
基金
国家自然科学基金(60073053)
教育部博士点基金
