Intelligent nonconvex compressive sensing using prior information for image reconstruction by sparse representation
作者: Qiang WangDan LiYi Shen
作者单位: 1Control Science and Engineering, Harbin Institute of Technology, No.92, West Da-Zhi Street, Nangang District, Harbin 150001, China
刊名: Neurocomputing, 2017, Vol.224 , pp.71-81
来源数据库: Elsevier Journal
DOI: 10.1016/j.neucom.2016.10.051
关键词: Image reconstructionSparse representationNonconvex l 0 minimizationIntelligent optimizationPrior information
原始语种摘要: Abstract(#br)Image reconstruction by sparse representation, which is based on the fact that natural images are intrinsically sparse under some over-completed dictionaries, has shown promising results in many applications. However, due to the down-sampled measurements, the results of image reconstruction by sparse representation are sometimes not accurate enough. In this paper, we propose a novel intelligent nonconvex compressive sensing (INCS) algorithm using prior information for image reconstruction by sparse representation. First of all, the over-completed dictionary of Ridgelet is used to introduce the sparse level for each image block. Then we use the nonlocal self-similarity property and joint sparsity to obtain the basic prior information to guide the reconstruction, which...
全文获取路径: Elsevier  (合作)
影响因子:1.634 (2012)

  • reconstruction 复原
  • image 
  • sparse 稀疏的
  • information 报告
  • representation 表现
  • intelligent 有理性的
  • sensing 感觉
  • computational 计算的
  • minimization 最小化
  • propose 提议