Smooth Constrained OSEM Iteration Reconstruction Algorithm

Yanhong, 张艳红 Zhang; Liang, 盛亮 Sheng; Kuinian, 李奎念 Li; Yang, 李杨 Li; Bodong, 彭博栋 Peng; Mei, 张美 Zhang; Jizhen, 赵吉祯 Zhao; Yuan, 袁媛 Yuan

doi:10.3788/lop54.021006

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has been shown that the ordered subsets expectation maximization (OSEM) is a more rapid and practical convergent iterative algorithm. The modification algorithm named TVM-OSEM has better advantages in reconstruction; specifically, the boundary is clearer and the reconstruction quality is higher [10]. This paper proposes using TVM-OSEM to reconstruct the integral image of multichannel discharge in an annual electrode, and discusses requirements for the number of projections and the angular position of the views that make the reconstruction solution reliable.…”

Section: Introductionmentioning

confidence: 99%

A novel method for multichannel discharge image diagnosis in gas switches based on multi-axis tomography techniques

Zhang

Sheng

et al. 2023

Plasma Sci. Technol.

View full text Add to dashboard Cite

The discharge channel is a crucial diagnostic tool for the properties of gas switch. However, the single-view images is hardly to understand the inherently the discharge channel characteristics of trigger electrode. A reconstruction method of the discharge channel in gas switch is proposed based on multi-axis tomography technique, which uses very few projections of optical image to reconstruct the location of multichannel discharge in annular electrodes. In this paper, we present an algorithm, TVM-OSEM (total variation minimization ordered subsets expectation maximization) effectively removes artifacts on the reconstructed images to improve the accuracy of the diagnosis of discharge channels. The method is validated against simulations of radiator with high-density-difference boundary. The calculated discharge channel distribution from one experiment is presented.

show abstract

Section: Introductionmentioning

confidence: 99%