数据采集与处理2011，Vol.26Issue(3)：292-299,8.
基于小波域隐马尔可夫模型的信号超分辨率重建
Signal Super-Resolution Reconstruction Based on Wavelet-Domain HMM
摘要
Abstract
A signal super-resolution reconstruction algorithm based on wavelet-domain hidden Markov model (WHMM) is proposed. According to the Bayesian principle and the maximum a posteriori probability estimation theory, a signal super-resolution reconstruction model is obtained using WHMM as the prior knowledge. The Euler-Lagrange equation of the reconstruction problem and the differential of log-likelihood function are deduced in detail, and a concise linear equation is concluded to solve the signal super-resolution problem. Finally, the expectation maximization (EM) algorithm and the conjugate gradient algorithm are adopted to compute WHMM parameters and high resolution signal, alternately. Experimental results with one and two dimensional signals demonstrate that the presented method can preserve more high-frequency details while denoising. Under the same degradation, for one dimensional test signals, the peak signal-to-noise-ratio (PSNR) values of the proposed algorithm are averagely increased by about 2.399 4 dB and 4.474 2 dB compared with cubic interpolation and Tikhonov regularization, respectively; for two dimensional test signals, the PSNR improvements are 1.174 1 dB and 0.648 7 dB compared with bicubic interpolation and Tikhonov regularization,respectively.关键词
超分辨率重建/小波变换/隐马尔可夫模型/期望最大化算法/共轭梯度算法Key words
super-resolution reconstruction/ wavelet transform, hidden Markov model/ expectation maximization algorithm/ conjugate gradient algorithm分类
信息技术与安全科学引用本文复制引用
韩玉兵,陈如山,吴乐南..基于小波域隐马尔可夫模型的信号超分辨率重建[J].数据采集与处理,2011,26(3):292-299,8.基金项目
国家自然科学基金(60802039)资助项目 （60802039）
江苏省博士后科研计划(0702023B)资助项目. （0702023B）
