Journal of South China University of Technology (Natural Science Edition) ›› 2017, Vol. 45 ›› Issue (10): 108-113.doi: 10.3969/j.issn.1000-565X.2017.10.015

• Electronics, Communication & Automation Technology • Previous Articles     Next Articles

Blind Image De-blurring Based on Sparse Prior and Relative Total Variation

YAO Ruo-he1 RAO Jing-song1 LIU Wei-jian2   

  1. 1.School of Electronic and Information Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China; 2.VTRON Technology Company,Guangzhou 510670,Guangdong,China
  • Received:2016-11-29 Revised:2017-02-25 Online:2017-10-25 Published:2017-09-01
  • Contact: 姚若河(1961-)，男，教授，博士生导师，主要从事集成电路系统设计、数字信号处理及应用研究． E-mail:phryao@scut.edu.cn
  • About author:姚若河(1961-)，男，教授，博士生导师，主要从事集成电路系统设计、数字信号处理及应用研究．
  • Supported by:
     Supported by the Science and Technology Research Projects of Guangdong Province(2015B090909001)

PDF

290

Abstract: In a blind image de-blurring,estimating a blur kernel from a single blurred image is a severely ill-posed problem.In this paper,a blind image de-blurring method based on sparse prior and relative total variation is pro- posed.In this method,a smoothing algorithm based on weighted L0 is employed to adaptively extract the main structure of an image and remove such factors adverse to a kernel estimation as the noise,the details and the edges of a small object,and a relative total variation method is adopted to overcome the inaccuracy of estimating a com- plex blur kernel by means of the regularization method of the sparse prior.Moreover,the regularization method of the Super-Laplacian prior is used to estimate the latent image.Experimental results show that，as compared with the existing image de-blurring method,the proposed method helps achieve the latent image of better structure and less artifacts,and it can better recover the latent image.

Key words: weighted L0, kernel estimation, relative total variation, motion de-blurring, sparse prior, salient edge

CLC Number: