CLC number: TN911.73
On-line Access:
Received: 2009-01-06
Revision Accepted: 2009-04-24
Crosschecked: 2009-10-18
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Wen CHEN, Xiang-zhong FANG, Yan CHENG. A maximum a posteriori super resolution algorithm based on multidimensional Lorentzian distribution[J]. Journal of Zhejiang University Science A, 2009, 10(12): 1705-1713.
@article{title="A maximum a posteriori super resolution algorithm based on multidimensional Lorentzian distribution",
author="Wen CHEN, Xiang-zhong FANG, Yan CHENG",
journal="Journal of Zhejiang University Science A",
volume="10",
number="12",
pages="1705-1713",
year="2009",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0920013"
}
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%T A maximum a posteriori super resolution algorithm based on multidimensional Lorentzian distribution
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%A Yan CHENG
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0920013
TY - JOUR
T1 - A maximum a posteriori super resolution algorithm based on multidimensional Lorentzian distribution
A1 - Wen CHEN
A1 - Xiang-zhong FANG
A1 - Yan CHENG
J0 - Journal of Zhejiang University Science A
VL - 10
IS - 12
SP - 1705
EP - 1713
%@ 1673-565X
Y1 - 2009
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A0920013
Abstract: This paper presents a threshold-free maximum a posteriori (MAP) super resolution (SR) algorithm to reconstruct high resolution (HR) images with sharp edges. The joint distribution of directional edge images is modeled as a multidimensional Lorentzian (MDL) function and regarded as a new image prior. This model makes full use of gradient information to restrict the solution space and yields an edge-preserving SR algorithm. The Lorentzian parameters in the cost function are replaced with a tunable variable, and graduated nonconvexity (GNC) optimization is used to guarantee that the proposed multidimensional Lorentzian SR (MDLSR) algorithm converges to the global minimum. Simulation results show the effectiveness of the MDLSR algorithm as well as its superiority over conventional SR methods.
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