针对电容层析成像技术应用于气固两相流检测时,图像重建过程中存在的不适定性问题,提出一种稀疏松弛正则化回归模型(SR3)应用于ECT图像重建。采用软阈值迭代法和梯度下降法为SR3模型求解器,向SR3模型中加入L1、L2惩戒项,并设计滤值环节优化解向量。实验结果表明,改进SR3模型算法相比Tikhonov正则化算法、L1正则化算法及原SR3模型算法,重建图像精度明显提高,图像相对误差显著降低,有较好的成像效果。
Abstract
In order to solve the problem of discomfort in image reconstruction, a sparse relaxation regularized regression model (SR3) was proposed for ECT image reconstruction when capacitance tomography was applied to gas-solid two-phase flow detection. Soft threshold iteration method and gradient descent method were used as solvers for SR3 model, L1 and L2 penalty terms were added to SR3 model, and filter element was designed to optimize solution vector. Experimental results show that compared with Tikhonov regularization algorithm, L1 regularization algorithm and the original algorithm of SR3 model, the algorithm of improved SR3 model has significantly improved the reconstructed image accuracy, significantly reduced the relative error of the image, and has better imaging effect.
关键词
计量学 /
电容层析成像 /
SR3模型算法 /
正则化 /
图像重建算法 /
多相流
Key words
metrology /
capacitance tomography /
SR3 model algorithm /
regularization /
algorithm of imaging reconstruction /
multiphase flow
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基金
国家自然科学基金(61871379);天津市教委科研计划(2020KJ012)
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