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Photoacoustic image reconstruction based on Bayesian compressive sensing algorithm

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Abstract

The photoacoustic tomography (PAT) method, based on compressive sensing (CS) theory, requires that, for the CS reconstruction, the desired image should have a sparse representation in a known transform domain. However, the sparsity of photoacoustic signals is destroyed because noises always exist. Therefore, the original sparse signal cannot be effectively recovered using the general reconstruction algorithm. In this study, Bayesian compressive sensing (BCS) is employed to obtain highly sparse representations of photoacoustic images based on a set of noisy CS measurements. Results of simulation demonstrate that the BCS-reconstructed image can achieve superior performance than other state-of-the-art CS-reconstruction algorithms.

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所属栏目:Image processing

收稿日期:2010-12-17

录用日期:2011-01-14

网络出版日期:2011-05-06

作者单位    点击查看

孙明健:Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
冯乃章:Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
沈毅:Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
李建刚:Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
马立勇:Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
伍政华:Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China

联系人作者:联系作者(sunmingjian@hit.edu.cn)

备注:This work was supported by the National Natural Science Foundation of China (No. 30800240), the Shandong Provincial Key Science-Technology Project (No. 2009GG10001006), the Shandong Provincial Promotive Research Fund for Excellent Young and Middle-Aged Scientists (No. BS2010DX001), and the Weihai City Science & Technology Development Project (No. 2010-3-96).

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