A SVM-Based Blur Identification Algorithm for Image Restoration and Resolution Enhancement

  • Jianping Qiao
  • Ju Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4252)


Blur identification is usually necessary in image restoration. In this paper, a novel blur identification algorithm based on Support Vector Machines (SVM) is proposed. In this method, blur identification is considered as a multi-classification problem. First, Sobel operator and local variance are used to extract feature vectors that contain information about the Point Spread Functions (PSF). Then SVM is used to classify these feature vectors. The acquired mapping between the vectors and corresponding blur parameter provides the identification of the blur. Meanwhile, extension of this method to blind super-resolution image restoration is achieved. After blur identification, a super-resolution image is reconstructed from several low-resolution images obtained by different foci. Simulation results demonstrate the feasibility and validity of the method.


Support Vector Machine Support Vector Regression Point Spread Function Training Image Image Restoration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jianping Qiao
    • 1
  • Ju Liu
    • 1
  1. 1.School of Information Science and EngineeringShandong UniversityJinanChina

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