Search

Article

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

Image sparsity evaluation based on principle component analysis

Ma Yuan Lü Qun-Bo Liu Yang-Yang Qian Lu-Lu Pei Lin-Lin

Image sparsity evaluation based on principle component analysis

Ma Yuan, Lü Qun-Bo, Liu Yang-Yang, Qian Lu-Lu, Pei Lin-Lin
PDF
Get Citation

(PLEASE TRANSLATE TO ENGLISH

BY GOOGLE TRANSLATE IF NEEDED.)

Metrics
  • Abstract views:  1030
  • PDF Downloads:  1599
  • Cited By: 0
Publishing process
  • Received Date:  29 May 2013
  • Accepted Date:  30 July 2013
  • Published Online:  05 October 2013

Image sparsity evaluation based on principle component analysis

  • 1. Key Laboratory of Computational Optics Imaging Technology, Academy of Opto-electronics, Chinese Academy of Sciences, Beijing 100094, China;
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
Fund Project:  Project supported by the National Science Fund for Distinguished Young Scholars of China (Grant No. 61225024) and the National High Technology Research and Development Program of China (Grant No. 2011AA7012022).

Abstract: In compressive sensing, signal sparsity is an important parameter which influences the number of data sampling in reconstruction process and the quantity of the reconstructed result. But in practice, undersampled and oversampled phenomenon will occur because of the unknown sparsity, which may lose the advantages of compressive sensing. So how to determine the image sparsity quickly and accuratly is significant in the compressive sensing process. In this paper, we calculate the image sparsity based on the data acquired during compressive sensing recontruction projection which sparses the origin image in wavelets domain, but we find that its procession is complex, and the final results are seriously influenced by wavelet basis function and the transform scales. We then introduce the principle component analysis (PCA) theory combined with compressive sensing, and establish a linear relationship between image sparsity and coefficient founction variance based on the assumption that PCA is of approximately normal distribution. Multiple sets of experiment data verify the correctness of the linear relationship mentioned above. Through previous analysis and simulation, the sparsity estimation based on PCA has an important practical value for compressive sensing study.

Reference (18)

Catalog

    /

    返回文章
    返回