基于改进的期望值最大化自适应光学图像多帧迭代去卷积算法

doi:10.3969/j.issn.1000-1093.2014.11.006

兵工学报 ›› 2014, Vol. 35 ›› Issue (11): 1765-1773.doi: 10.3969/j.issn.1000-1093.2014.11.006

基于改进的期望值最大化自适应光学图像多帧迭代去卷积算法

张丽娟^1,2，杨进华¹，苏伟³，姜成昊¹，王晓坤¹，谭芳¹

(1.长春理工大学光电工程学院, 吉林长春 130022; 2.长春工业大学计算机科学与工程学院, 吉林长春 130012;

收稿日期:2014-02-01 修回日期:2014-02-01 上线日期:2015-01-05
通讯作者: 张丽娟 E-mail:ldm0214@163.com
作者简介:张丽娟（1978—），女，副教授，博士研究生
基金资助:
国家自然科学基金项目（51202017）;吉林省教育厅“十二五”科学技术研究项目(2013145、201363)

Multi-frame Iteration Blind Deconvolution Algorithm Based on Improved Expectation Maximization for Adaptive Optics ImageRestoration

ZHANG Li-juan^1,2， YANG Jin-hua¹， SU Wei³， JIANG Cheng-hao¹， WANG Xiao-kun¹， TAN Fang¹

(1.School of Opto-electronic Engineering, Changchun University of Science and Technology, Changchun 130022, Jilin, China;2.College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, Jilin, China;3.Network Information Center , Changchun University of Science and Technology, Changchun 130022, Jilin, China)

Received:2014-02-01 Revised:2014-02-01 Online:2015-01-05
Contact: ZHANG Li-juan E-mail:ldm0214@163.com

摘要/Abstract

摘要： 为提高自适应光学图像复原的效果，基于期望值最大化理论，提出了一种基于改进期望值最大化（EM）算法的自适应光学（AO）图像多帧联合去卷积算法。通过建立多帧AO图像退化的数学模型，推导出基于相位误差并随时间变化的点扩散函数(PSF)模型，根据图像功率谱密度及约束图像支持域的方法对AO图像进行去噪处理。应用AO成像系统参数与正则化技术相结合对EM算法进行改进，建立多帧AO图像联合去卷积的代价函数及其参数估计的优化模型。利用所建模型对模拟图像和实际观测的AO图像进行图像复原实验验证文中算法的复原效果。实验结果表明，与Wiener迭代盲去卷积、Richardson-Lucy迭代盲去卷积算法相比，文中算法迭代次数减少14.3%，估算精度有了明显提高，辨识出了AO图像的PSF，复原出了清晰的观测目标图像。研究结果对实际AO图像复原有一定的应用价值。

关键词: 光学, 自适应光学图像, 大气湍流, 最大似然函数, 功率谱密度, 点扩散函数, 期望值最大化

Abstract: To improve the effect of adaptive optics image restoration, a deconvolution algorithm based on the improved expectation-maximization (EM) algorithm is proposed according to the EM theory. A mathematical model for degenerating the multi-frame adaptive optics images is built. The point spread function (PSF) model changed over time based on phase error is deduced. The AO images are de-noised using the image power spectrum density and the support constraints. The EM algorithm is improved by combining the AO imaging system parameters and regularization technique. A cost function for the joint-deconvolution of multi-frame AO images is given, and an optimization model for their parameter estimation is built. The image-restoration experiments of the analog images and the real AO images are performed to verify the image restoration effect of the proprosed algorithm. The experimental results show that, compared with the Wiener-IBD or RL-IBD algorithm, the iterations of the proposed algorithm is decreased by 14.3%, and its estimation accuracy is significantly improved. The model distinguishes PSFs of the AO images and recovers the observed target images clearly.

Key words: optics, adaptive optics image, atmospheric turbulence, maximum-likelihood function, power spectral density, point spread function, expectation maximization

中图分类号:

TP751

张丽娟，杨进华，苏伟，姜成昊，王晓坤，谭芳. 基于改进的期望值最大化自适应光学图像多帧迭代去卷积算法[J]. 兵工学报, 2014, 35(11): 1765-1773.

ZHANG Li-juan， YANG Jin-hua， SU Wei， JIANG Cheng-hao， WANG Xiao-kun， TAN Fang. Multi-frame Iteration Blind Deconvolution Algorithm Based on Improved Expectation Maximization for Adaptive Optics ImageRestoration[J]. Acta Armamentarii, 2014, 35(11): 1765-1773.

参考文献

［1］李大禹,胡立发,穆全全，等.高准确度LCOS自适应光学成像系统的研究[J]. 光子学报，2008,37(3):506-508.
LI Da-yu，HU Li-fa， MU Quan-quan，et al. A high-resolution liquid crystal adaptive optics system[J]. Acta Photonica Sinica, 2008,37(3):506-508.（in Chinese）
[2] 饶长辉,沈锋,姜文汉.自适应光学系统波前校正残余误差的功率谱分析方法[J].光学学报,2000,20(1):68-73.
RAO Chang-hui, SHEN Feng, JIANG Wen-han. Analysis of closed-loop wavefront residual error of adaptive optical system using the method of power spectrum[J]. Acta Optica Sinica, 2000, 20(1):68-73.（in Chinese）
[3] 陈波.自适应光学图像复原理论与算法研究[D]. 郑州：解放军信息工程大学,2008:44-46
CHEN Bo. The theory and algorithms of adaptive optics image restoration[D]. Zhengzhou：PLA Information Engineering University，2008:44-46.(in Chinese）
[4] Tian Y, Rao C H, Wei K. Postprocessing of adaptive optics images based on frame selection and multiframe blind deconvolution [C]∥Adaptive Optics Systems. Marseille, France: SPIE, 2008.
[5] Schulz T J. Nonlinear models and methods for space-object imaging through atmospheric turbulence[C]// The 1996 IEEE International Conference on Image Processing.Lausanne,Switzerland: IEEE，1996.
[6] 路雅宁,郭雷,李晖晖. 带限剪切波变换与全变差结合的图像去噪[J].光子学报,2013,42(12):1430-1435.
LU Ya-ning, GUO Lei, LI Hui-hui. Total variation based band-limited sheralets transform for image denoising [J]. Acta Photonica Sinica, 2013,42(12):1430-1435.(in Chinese）
[7] 赵杰，杨建雷．基于 Cycle Spinning Contourlet 变换和总变分最小化的遥感图像去噪算法[J]．光子学报，2010,39(9):1659-1660.
ZHAO Jie, YANG Jian-lei. Remote sensing image denoising algorithm based on fusion theory using cycle spinning contourlet transform and total variation minimization [J]. Acta Photonica Sinica, 2010,39(9):1659-1660.（in Chinese）
[8] Matson C L,Roggemann M C . Noise reduction in adaptive optics imagery with the use of support constraints[J]. Applied Optics, 1995, 34(5):767-780.
[9] Tyler D W, Matson C L. Reduction of nonstationary noise in telescope imagery using a support constraint [J].OSA Optics Express, 1997, 1(11): 347-350.
[10] Zhang L J, Yang J H, Su W. Research on blind deconvolution algorithm of multiframe turbulence-degraded images[J]. Journal of Information and Computational Science, 2013, 10(12):3625-3633.
[11] Dempster A P, Laird N M, Rubin D B. Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of the Royal Statistical Society Series B, 1977, 39(1):2-20.
[12] Mugnier L M, Conan J M, Fusco T， et al. Joint maximum S posteriori estimation of object and PSF for turbulence degraded images[C]// Bayesian Inference for Inverse Problems. San Diego, California： SPIE, 1998.
[13] 洪汉玉.成像探测系统图像复原算法研究[D]. 武汉：华中科技大学，2004：51-107.
HONG Han-yu. Research on image restoration algorithms in imaging detection system[D]. Wuhan：Huazhong University of Science and Technology, 2004:51-107.（in Chinese）
[14] Tsumuraya F. Iterative blind deconvolution method using Lucy’s algorithm[J]. Astronamy and Astrophysics, 1994, 282(2):699-708.
[15] 张士杰,李俊山，杨亚威，等. 湍流退化红外图像降晰函数辨识[J]. 光学精密工程,2013,21(2):514-520.
ZHANG Shi-jie, LI Jun-shan, YANG Ya-wei, et al. Blur identification of turbulence-degraded IR images[J]. Optics and Precision Engineering, 2013,21(2):514-520.(in Chinese）
[16] 陈波,程承旗,郭仕德, 等. 自适应光学图像非对称图像迭代盲复原算法[J]. 强激光与粒子束, 2011,23(2):313-318.
CHEN Bo, CHENG Cheng-qi, GUO Shi-de, et al. Unsymmetrical multi-limit iterative blind deconvolution algorithm for adaptive optics image restoration[J]. High Power Laser and Particle Beams, 2011,23(2):313-318.(in Chinese）

[1]	陶宗慧, 刘唯奇, 陈亚楠, 倪小龙, 娄岩, 刘显著, 姜会林. 大气信道激光通信系统光束偏振特性[J]. 兵工学报, 2022, 43(3): 481-488.
[2]	刘欢，王春艳，王志强，孙昊，赵义武. 色温星模拟器用抛物面离轴反射光学系统设计[J]. 兵工学报, 2021, 42(3): 572-580.
[3]	余松林，陈玉华，何鹄，王吉军，李岩. 基于颜色聚类的光学伪装效果评估背景选取方法[J]. 兵工学报, 2021, 42(3): 617-624.
[4]	李海廷，胡鑫，曾双，李少波，周国家，高志峰. 大气湍流对半主动激光制导中光斑检测精度的影响[J]. 兵工学报, 2021, 42(2): 297-307.
[5]	王丹艺，蒋山平，杨林华. 基于军用飞机的热流模拟辐照系统[J]. 兵工学报, 2020, 41(9): 1913-1920.
[6]	丁贵鹏，陶钢，庞春桥，王小峰. 焦深延拓的透雾连续变焦光学系统设计[J]. 兵工学报, 2020, 41(8): 1539-1548.
[7]	卢莉萍，郑潇. 基于旋转反射镜光学成像的动态目标跟踪方法[J]. 兵工学报, 2020, 41(8): 1558-1565.
[8]	翟岩, 姜会林, 梅贵. 具有微小角度零件的加工及检验方法[J]. 兵工学报, 2020, 41(7): 1417-1422.
[9]	景文博，黄炳坤，刘健，于洪洋. 激光照射性能测量中异源相机的标定方法[J]. 兵工学报, 2020, 41(5): 924-931.
[10]	张宝振，王汉平，张哲，程梦文. 二维路面不平度的分形构造方法[J]. 兵工学报, 2020, 41(12): 2389-2396.
[11]	李春明，姜雨彤，宋海平，纪超，郭猛，朱琳. 基于光学厚度代理模型的雾浓度估计及图像去雾[J]. 兵工学报, 2019, 40(7): 1425-1433.
[12]	张绍军，薛向尧，高云国，王光. 新型光学检测靶标静态指向误差修正[J]. 兵工学报, 2019, 40(5): 1030-1037.
[13]	梁静，贾慧民，苏瑞巩，唐吉龙，房丹，冯海通，张宝顺，魏志鹏. 垂直腔面发射半导体激光器腔模位置对器件输出波长的影响研究[J]. 兵工学报, 2019, 40(12): 2482-2487.
[14]	安喆，徐熙平，杨进华，乔杨，吕耀文，刘洋. 光学透射式汽车抬头显示器系统的虚实注册方法研究[J]. 兵工学报, 2018, 39(5): 1006-1011.
[15]	马艳彤，郑荣，韩晓军. 面向海底光学探测使命的自治水下机器人水平路径跟随控制[J]. 兵工学报, 2017, 38(6): 1147-1153.

基于改进的期望值最大化自适应光学图像多帧迭代去卷积算法

Multi-frame Iteration Blind Deconvolution Algorithm Based on Improved Expectation Maximization for Adaptive Optics ImageRestoration

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价