【摘要】 为了改善逆问题病态性又能提高图像重建质量,提出了一种基于模拟退火粒子群算法的MIT图像重建方法.根据Hessian矩阵的维度,构建了一种Tikhonov和NOSER型混合多参数正则化算法.将模拟退火算法和粒子群算法进行组合,以广义交叉准则构建目标函数,进行正则化多参数寻优.结果表明,所提方法不仅有效克服了MIT重建图像数值解的不稳定性,增强了抗噪性能,而且所获得的重建图像的质量优于Tikhonov正则化和混合正则化算法,为M IT技术应用提供了理论参考.更多还原

【Abstract】 In order to improve the ill-posed inverse problem and improve the quality of image reconstruction,a MIT image reconstruction method based on simulated annealing and particle swarm optimization was proposed.According to the dimensions of the Hessian matrix,a Tikhonov and NOSER hybrid multi-parameter regularization algorithm was constructed.The simulated annealing algorithm and particle swarm algorithm were combined,the objective function was constructed by the generalized cross criterion,and the regularized multi-arameter optimization was performed.The results show that not only the proposed method effectively overcomes the instability of the numerical solution of the MIT reconstructed image and enhances the anti-noise performance,but also the quality of the obtained reconstructed image is better than that of Tikhonov regularization and hybrid regularization algorithms,which provides a theoretical reference for the application of MIT technology.更多还原