基于隐马尔科夫树模型的小波域压缩采样信号重构方法

压缩传感理论利用信号的稀疏性,对其非自适应线性投影进行压缩采样,通过最优化问题准确重构原始信号。传统重构算法仅利用了信号的稀疏性,而未对转换后的信号结构进行分析。提出了一种基于4状态的隐马尔科夫树模型的小波域压缩采样信号的重构方法,相对2状态的隐马尔科夫树模型,该模型能够获取相邻尺度小波系数的更多相关特性,通过仿真结果表明,该算法具有更高的重构精度。

Compressive sampling signal reconstruction in wavelet-domain based on hidden Markov tree model

Compressed sensing theory enables the reconstruction of sparse signal from a small number of non-adaptives linear projections.Conventional reconstruction algorithm involves linear programming or greedy algo-rithms,these reconstruction techniques are generic and assume no particular structure in the signal aside from sparsity.The compressive sampling signal reconstruction in wavelet domain is inspired based on tow-state wavelet hidden Markov tree model.In this paper,we propose a four-state wavelet Hidden Markov Tree model,it can capture more in-terscales dependencies of wavelet coefficients between two neighboring scales,the simulation shows that it reconstruc-tion precision is improved.