MNR图像重建算法中正则化因子研究

为了提高电阻层析成像图像重建算法求解逆问题精度，对修正牛顿-拉夫逊算法中正则化因子进行了研究。借鉴改进粒子群算法中惯性权重递减策略，根据算法迭代过程中成像精度，自动更新正则化因子的最大值，提出一种新的改进牛顿-拉夫逊图像重建算法，应用于两相流典型流型——层状流、泡状流、环状流、中心流及复合流型图像重建。仿真实验结果表明，相同实验条件下，相比迭代线性反投影算法、修正牛顿-拉夫逊算法，新算法有效提高了图像重建精度。

Research on regularization factor of Modified-Newton-Raphson algorithm for image reconstruction

Aiming to improve the precision of the image reconstruction algorithm when solving the inverse problem in electrical resistance tomography,research on the regularization factor of modified-Newton-Raphson algorithm is carried out.Motivated by the idea of decreasing inertia weight used in the improved particle swarm optimization,the upper bound of the regularization factor is updated automatically according to the imaging quality during the iteration process.Hence a novel improved Newton-Raphson reconstruction algorithm is proposed and applied to image reconstruction of two-phase typical flow regimes—stratified flow,bubble flow,annular flow,core flow and compound flow.Simulation results demonstrate that,compared with iterative linear back projection algorithm and modified Newton-Raphson algorithm,the new proposed algorithm can improve the imaging accuracy effectively under the same experimental condition.