【摘要】 为增加信号重构的可信度和减少重构过程的人为干预,采用贝叶斯压缩感知的方法,将待重构信号赋予先验分布,不仅重构出信号参数,并能同时获得信号参数的置信区间,以此实时调整重构模型使信号恢复达到最佳。基于拉普拉斯分级先验模型的贝叶斯压缩感知算法,对图像进行合理分块,用不同比率对分块图像压缩,并在重构过程进行分级处理,进一步减少运算时间,最终使用相关向量机(RVM)实现了稀疏信号的最大后验概率估计。实验结果表明,通过和传统算法相比较,上述算法使得重构图像质量得到明显提高,并且相比于全局贝叶斯压缩感知算法具有更好的实时性。更多还原

【Abstract】 Bayesian Compressive Sensing( BCS) can reconstruct the signal by improving the performance of certainty and reducing human intervention. It provides posterior distribution of the parameter rather than point estimate,so we can get the uncertainty of the estimation to optimize the data reconstruction process adaptively. In this paper,we present hierarchical form of Laplace prior,and aiming at improving the efficiency of reconstruction,we segment image into blocks,employ various sample rates to compress different kinds of block and utilize relevance vector machine( RVM) to sparse signal in the reconstruction process. At last,we provide experimental result of image,and compare with the state-of-the-art CS algorithms and Bayesian CS,it demonstrating the superior effectiveness and real-time performance of the proposed approach.更多还原