基于自适应步长双参数正则化算法的超声波过程层析成像图像重建  被引量:2

Image Reconstruction of Ultrasonic Process Tomography Based on Fast Two-parameter Regularization Algorithm

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作  者:张琳[1] 邵富群[1] 周明[1] 

机构地区:[1]东北大学信息科学与工程学院

出  处:《计量学报》2015年第1期48-53,共6页Acta Metrologica Sinica

摘  要:提出了一种新的自适应步长双参数正则化算法,对超声波层析成像系统检测浆体浓度分布进行图像重建。该算法利用转换矩阵将超定解作为先验信息,嵌入到正则化泛函中,避免重建图像被过度平滑,不仅成像速度较快且重建图像具有较高分辨率。仿真实验结果表明,相比于Tikhonov正则化算法以及Landweber算法,自适应步长双参数正则化算法重建图像的相关系数有明显提高并且边界信息更加可靠。A new adapted step two-parameter regularization algorithm is presented to reconstruct image in ultrasound tomography system for detecting distribution of slurry concentration. The overdetermined solution, as a priori information, is embedded into the regularization function by using the transition matrix for accelerate reconstruction. Higher space resolution is achieved and the over-smoothing deficiency of the reconstruction can be avoided effectively. The simulation results show that, compared to Tikhonov regularization algorithm and Landweber algorithm, the correlation coefficient of the reconstructed image by using SATPR are significantly improved and the boundary information of image is more reliable.

关 键 词:计量学 超声波过程 自适应步长 双参数正则化算法 先验信息 层析成像 图像重建 

分 类 号:TB937[一般工业技术—计量学]

 

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