›› 2012, Vol. 42 ›› Issue (04): 1054-1058.

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Weighted mixed-norm based blind super-resolution algorithm

LIU Gang1,2, ZHAO Hong-yi3, HU Zhen-long4   

  1. 1. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China;
    2. School of Electronics and Information, Shanghai Dianji University, Shanghai 201306, China;
    3. Faculty of High Vocational Education, Xi'an University of Technology, Xi'an 710082, China;
    4. Zhejiang Yuexiu University of Foreign Languages, Shaoxing 312008, China
  • Received:2011-04-03 Online:2012-07-01 Published:2012-07-01

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Abstract: Conventional Super-Resolution (SR) methods have some limitations. First, most of existing SR algorithms can not cope with local motions and hence not suitable for most video sequences. Second, the blurring operator is assumed to be known in advance and be a constant for all the low-resolution images. Finally, SR noise is assumed to be either Gaussian or Laplacian. To overcome these limitations, a general local cost function is proposed that consists of weighted L1-and L2-norms considering the SR noise model. In this function, the weights are generated according to the error of registration and noise distribution, and the inaccurately registered parts of the image are penalized. Both the super-resolved images and blurring operators are estimated at the same time. Both objective and subjective evaluations demonstrate the effectiveness of the proposed algorithm.

Key words: information processing, super-resolution, image registration, image fusion

CLC Number: 

  • TN911.73
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