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Semiblind Image Deconvolution with Spatially Adaptive Total Variation Regularization

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  • Yaduan Ruan
  • Houzhang Fang
  • Qimei Chen

Abstract

A semiblind image deconvolution algorithm with spatially adaptive total variation (SATV) regularization is introduced. The spatial information in different image regions is incorporated into regularization by using the edge indicator called difference eigenvalue to distinguish flat areas from edges. Meanwhile, the split Bregman method is used to optimize the proposed SATV model. The proposed algorithm integrates the spatial constraint and parametric blur-kernel and thus effectively reduces the noise in flat regions and preserves the edge information. Comparative results on simulated images and real passive millimeter-wave (PMMW) images are reported.

Suggested Citation

  • Yaduan Ruan & Houzhang Fang & Qimei Chen, 2014. "Semiblind Image Deconvolution with Spatially Adaptive Total Variation Regularization," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-8, August.
    • Handle: RePEc:hin:jnlmpe:606170
    DOI: 10.1155/2014/606170
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