摘要
讨论了电子倍增CCD(EMCCD)图像的噪声来源及其统计特性,建立了混合泊松-高斯噪声分布模型。针对混合泊松-高斯噪声分布模型的极大似然函数难以求解的问题,对噪声模型进行了适当的初始化设置,利用期望最大化算法对噪声模型进行参数估计,有效实现了噪声参数的极大似然估计。Monte Carlo仿真结果及实验结果表明,期望最大化算法估计性能较好,对混合泊松-高斯分布有较好的拟合效果,能得到较高精度的参数估计值。
Based on the discussion of image noise sources and their statistic characteristics of the electron multiplying CCD （EMCCD）, the Poisson-Gaussian-mixture noise distribution model was established. Aiming at the problem that the solution of the maximum likelihood function of the Poisson-Gaussian- mixture distribution model was difficult to solve, the expectation-maximization method was proposed to estimate the parameters of Poisson-Gaussian-mixture noise distribution model of the EMCCD after appropriate initialization settings on the noise model, reducing the complexity of the parameter estimation and achieving equivalent effect of the maximum likelihood estimation. Monte Carlo simulation results and experimental results show that the expectation-maximization method can achieve good performance, provide satisfied fitting features for Poisson-Gaussian-mixture distribution, and obtain high precision parameter estimation values.
出处
《红外与激光工程》
EI
CSCD
北大核心
2013年第1期268-272,共5页
Infrared and Laser Engineering
基金
装备预研项目(40405030202)
微光夜视技术重点实验室(J20110505)
"紫金之星"资助项目
关键词
EMCCD
噪声分布模型
期望最大化算法
参数估计
EMCCD
noise distribution model
expectation-maximization method
parameter estimation
