Study of CT image reconstruction algorithm based on high order total variation
作者: Yarui XiZhiwei QiaoWenjie WangLei Niu
作者单位: 1School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China
刊名: Optik, 2020, Vol.204
来源数据库: Elsevier Journal
DOI: 10.1016/j.ijleo.2019.163814
关键词: High order total variationConstrained optimizationCompressed sensingImage reconstruction
原始语种摘要: Abstract(#br)The traditional total variation (TV) minimization algorithm is an image reconstruction algorithm based on compressed sensing, which can accurately reconstruct images from sparse data or highly noisy data and has been widely used in low-dose computed tomography (CT). Sometimes it may lead to staircase effect if the reconstructed image has not obvious piecewise constant feature. Recently, researches in the field of image processing suggested that the high order total variation (HOTV) can effectively suppress staircase effect. Whereas the HOTV reconstruction algorithm has not been carried out deeply and extensively in image reconstruction. Herein, we propose a HOTV reconstruction model and design its adaptive steepest descent-projection onto convex sets (ASD-POCS) solving...
全文获取路径: Elsevier  (合作)

  • reconstruction 复原
  • algorithm 算法
  • image 
  • variation 变异
  • phantom 受精台
  • reconstructed 修]重构[建
  • minimization 最小化
  • feature 结构元件
  • piecewise 分段
  • total 总和