基于正则先验的全变差快速代数迭代算法及其在火焰辐射测量中的重建性能分析 下载: 816次
ing at the problems of low precision and slow speed of the traditional reconstruction algorithms, we propose a regularization priori based fast all variation algebraic iteration (ARTTV) algorithm to improve the reconstruction precision of the symmetric and asymmetric flames. Further, to improve the reconstruction speed, we establish an extreme learning machine neural network based on the “ARTTV-particle swarm algorithm kernel”, which exhibits approximately the same reconstruction ability as that of the iterative algorithm. The construction speed of the proposed algorithm is approximately 300 times that of the iterative algorithm.
李明杰, 贺铸. 基于正则先验的全变差快速代数迭代算法及其在火焰辐射测量中的重建性能分析[J]. 光学学报, 2019, 39(10): 1012002. Mingjie Li, Zhu He. Regularization Priori Based Fast ARTTV Algorithm and Its Reconstruction Performance Analysis During Flame Radiation Measurement[J]. Acta Optica Sinica, 2019, 39(10): 1012002.