A maximum a posteriori super resolution algorithm based on multidimensional Lorentzian distribution  

作　　者：Wen CHEN Xiang-zhong FANG Yan CHENG 

机构地区：[1]Shanghai Key Lab of Digital Media Processing and Transmissions, Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, China [2]College of Information Technology, East China University of Political Science and Law, Shanghai 201620, China

出　　处：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》2009年第12期1705-1713,共9页浙江大学学报（英文版）A辑（应用物理与工程）

基　　金：Project (Nos 60705012 and 60802025) supported by the National Natural Science Foundation of China

摘　　要：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.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.

关 键 词：Edge preservation Multidimensional Lorentzian distribution （MDL） Super resolution THRESHOLD 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TN959.72[自动化与计算机技术—计算机科学与技术]