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Multiframe blind super resolution imaging based on blind deconvolution

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Abstract

As an ill-posed problem, multiframe blind super resolution imaging recovers a high resolution image from a group of low resolution images with some degradations when the information of blur kernel is limited. Note that the quality of the recovered image is influenced more by the accuracy of blur estimation than an advanced regularization. We study the traditional model of the multiframe super resolution and modify it for blind deblurring. Based on the analysis, we proposed two algorithms. The first one is based on the total variation blind deconvolution algorithm and formulated as a functional for optimization with the regularization of blur. Based on the alternating minimization and the gradient descent algorithm, the high resolution image and the unknown blur kernel are estimated iteratively. By using the median shift and add operator, the second algorithm is more robust to the outlier influence. The MSAA initialization simplifies the interpolation process to reconstruct the blurred high resolution image for blind deblurring and improves the accuracy of blind super resolution imaging. The experimental results demonstrate the superiority and accuracy of our novel algorithms.

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Authors and Affiliations

  1. School of Electronic Information Engineering, Tianjin University, Tianjin, 300072, China

    Wei Yuan  (元 伟) & Liyi Zhang  (张立毅)

  2. College of Information Engineering, Tianjin University of Commerce, Tianjin, 300134, China

    Liyi Zhang  (张立毅)

Authors
  1. Wei Yuan  (元 伟)
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  2. Liyi Zhang  (张立毅)
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Corresponding author

Correspondence to Wei Yuan  (元 伟).

Additional information

Supported by the National Natural Science Foundation of China(No. 61340034), the Research Program of Application Foundation and Advanced Technology of Tianjin(No.13JCYBJC15600).

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Yuan, W., Zhang, L. Multiframe blind super resolution imaging based on blind deconvolution. Trans. Tianjin Univ. 22, 358–366 (2016). https://doi.org/10.1007/s12209-016-2838-0

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  • DOI: https://doi.org/10.1007/s12209-016-2838-0

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