Exploiting Structure in Wavelet-Based Bayesian Compressive Sensing
Exploiting Structure in Wavelet-Based Bayesian Compressive Sensing
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DOI：
10.1109/tsp.2009.2022003
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发表时间：
2009-09-01
影响因子：
5.4
通讯作者：
Carin, Lawrence
中科院分区：
文献类型：
--
作者：
He, Lihan;Carin, Lawrence
针对小波基中稀疏的信号和图像，研究了贝叶斯压缩感知（CS）。在提出的模型中明确利用了小波系数的统计结构，因此，该框架超越了简单地假设数据在小波基础上是可压缩的。在小波系数中利用的结构与基于小波的压缩算法一致。通过马尔可夫链蒙特卡罗（MCMC）采样，建立了一个层次贝叶斯模型，并进行了有效的推理。该算法在几张自然图像上得到了充分的开发和演示，并与许多最先进的压缩感知反演算法进行了性能比较。
Bayesian compressive sensing (CS) is considered for signals and images that are sparse in a wavelet basis. The statistical structure of the wavelet coefficients is exploited explicitly in the proposed model, and, therefore, this framework goes beyond simply assuming that the data are compressible in a wavelet basis. The structure exploited within the wavelet coefficients is consistent with that used in wavelet-based compression algorithms. A hierarchical Bayesian model is constituted, with efficient inference via Markov chain Monte Carlo (MCMC) sampling. The algorithm is fully developed and demonstrated using several natural images, with performance comparisons to many state-of-the-art compressive-sensing inversion algorithms.