摘要
由于电阻层析成像问题本身严重病态,导致重建图像分辨率较低。为了提高重建图像精度,提出一种改进Landweber预迭代图像重建算法。首先,利用改进粒子群算法离线优化灵敏度矩阵,改善其病态程度,同时缩小满足算法收敛条件的增益因子取值范围;再设置4种典型流型,计算算法重建图像与其相关系数的平均值,并将平均值作为适应度函数,利用改进粒子群算法离线选择Landweber预迭代算法增益因子,并将改进算法应用于两相流典型流型与复合流型图像重建。实验结果表明,相同实验条件下,与在线预迭代(offline iteration online reconstruction,OIOR)算法、经验选择增益因子Landweber预迭代算法相比,新算法有效提高了图像重建质量,而与修正牛顿-拉夫逊算法相比,在不影响成像精度的前提下,提高了算法实时性。
Due to the ill-posedness of the image reconstruction problem in electrical resistance tomography,the space resolution of the reconstructed image is relatively low.In order to improve the imaging quality,this paper proposed an improved pre-iteration Landweber image reconstruction algorithm.The improved particle swarm optimization was firstly used off-line to improve the ill-posedness of the sensitivity matrix,and to limit the range of the gain factor which could guarantee the convergence of the algorithm;secondly,set four typical flow regimes,then calculated mean value of the four image correlation coefficients between the four pre-determined resistance distribution and their reconstructions,and regarded the mean value as the fitness function.Thereafter,the improved particle swarm optimization was used to calculate gain factor,and the proposed algorithm was applied to image reconstruction for both typical and complicated flow regimes of two-phase flow.The experimental results demonstrate that,under the same experimental conditions,compared to the offline iteration online reconstruction（OIOR） algorithm,pre-iteration Landweber method with empirically-chosen gain factor,the new method improves the imaging quality obviously;compared to the modified Newton-Raphson method,the improved algorithm enhances the real-time performance without sacrificing the imaging quality.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2013年第23期118-125,1,共8页
Proceedings of the CSEE
基金
国家自然科学基金项目(61201350
60820106002
50937005)
徐州市科技计划项目(XM12B078)
青蓝工程~~
关键词
电阻层析成像
病态
图像重建算法
灵敏度矩阵
增益因子
相关系数
electrical resistance tomography
ill-posedness
image reconstruction algorithm
sensitivity matrix
gain factor
correlation coefficient
