A maximum a posteriori super resolution algorithm based on multidimensional Lorentzian distribution |
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| Authors: | Wen Chen Xiang-zhong Fang Yan Cheng |
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| Institution: | (1) Bourns College of Engineering, University of California, Riverside, CA 92521, USA;(2) Lawrence Berkeley National Laboratory, University of California, Cyclotron Road 1, Berkeley, CA 94720, USA |
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| 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 Lor- entzian 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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| Keywords: | Edge preservation Multidimensional Lorentzian distribution (MDL) Super resolution Threshold |
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