照明系统是增强现实光学系统的重要组成部分,其体积、照度均匀性、能量利用率直接影响增强现实照明系统的质量,因此在照明系统中对光源二次配光非常重要。针对增强现实系统的自由曲面透镜形式和照明系统开展研究,重点分析准直系统集光...照明系统是增强现实光学系统的重要组成部分,其体积、照度均匀性、能量利用率直接影响增强现实照明系统的质量,因此在照明系统中对光源二次配光非常重要。针对增强现实系统的自由曲面透镜形式和照明系统开展研究,重点分析准直系统集光角度与体积的对应关系,在对光源准直系统的面型构建详细分析基础上,对中心透射边缘反射的折反射式准直系统的面型进行求解,结合偏微分方程法和划分网格法,设计了自由曲面透镜,该系统与偏振分光棱镜共同组成硅基液晶(liquid crystal on silicon,LCoS)照明系统。仿真分析结果表明:系统照度均匀性达到91.96%,若不计偏振影响,照明系统光学效率达到66.6%。该系统具有结构简单紧凑、体积小、质量轻、照度均匀性高等特点,满足增强现实眼镜的需求。展开更多
Recently,many variational models involving high order derivatives have been widely used in image processing,because they can reduce staircase effects during noise elimination.However,it is very challenging to construc...Recently,many variational models involving high order derivatives have been widely used in image processing,because they can reduce staircase effects during noise elimination.However,it is very challenging to construct efficient algo-rithms to obtain the minimizers of original high order functionals.In this paper,we propose a new linearized augmented Lagrangian method for Euler’s elastica image denoising model.We detail the procedures of finding the saddle-points of the aug-mented Lagrangian functional.Instead of solving associated linear systems by FFTor linear iterative methods(e.g.,the Gauss-Seidel method),we adopt a linearized strat-egy to get an iteration sequence so as to reduce computational cost.In addition,we give some simple complexity analysis for the proposed method.Experimental results with comparison to the previous method are supplied to demonstrate the efficiency of the proposed method,and indicate that such a linearized augmented Lagrangian method is more suitable to deal with large-sized images.展开更多
文摘照明系统是增强现实光学系统的重要组成部分,其体积、照度均匀性、能量利用率直接影响增强现实照明系统的质量,因此在照明系统中对光源二次配光非常重要。针对增强现实系统的自由曲面透镜形式和照明系统开展研究,重点分析准直系统集光角度与体积的对应关系,在对光源准直系统的面型构建详细分析基础上,对中心透射边缘反射的折反射式准直系统的面型进行求解,结合偏微分方程法和划分网格法,设计了自由曲面透镜,该系统与偏振分光棱镜共同组成硅基液晶(liquid crystal on silicon,LCoS)照明系统。仿真分析结果表明:系统照度均匀性达到91.96%,若不计偏振影响,照明系统光学效率达到66.6%。该系统具有结构简单紧凑、体积小、质量轻、照度均匀性高等特点,满足增强现实眼镜的需求。
基金supported by the NNSF of China grants 11526110,11271069,61362036 and 61461032,the 863 Program of China grant 2015AA01A302the Open Research Fund of Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing(2016WICSIP013)the Youth Foundation of Nanchang Institute of Technology(2014KJ021).
文摘Recently,many variational models involving high order derivatives have been widely used in image processing,because they can reduce staircase effects during noise elimination.However,it is very challenging to construct efficient algo-rithms to obtain the minimizers of original high order functionals.In this paper,we propose a new linearized augmented Lagrangian method for Euler’s elastica image denoising model.We detail the procedures of finding the saddle-points of the aug-mented Lagrangian functional.Instead of solving associated linear systems by FFTor linear iterative methods(e.g.,the Gauss-Seidel method),we adopt a linearized strat-egy to get an iteration sequence so as to reduce computational cost.In addition,we give some simple complexity analysis for the proposed method.Experimental results with comparison to the previous method are supplied to demonstrate the efficiency of the proposed method,and indicate that such a linearized augmented Lagrangian method is more suitable to deal with large-sized images.
