基于模拟退火粒子群算法的MIT图像重建方法

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MIT技术应用提供了理论参考.

MIT Image Reconstruction Method Based on Simulated Annealing Particle Swarm Algorithm

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-parameter 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.