大数据量红外图像非均匀性校正仿真研究
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摘要
对红外图像非均匀性进行校正能够提高红外图像质量,对实现红外图像信息的准确完整提取具有重要意义。针对当前红外目标图像校正方法存在的校正过程复杂,且校正效果较差的问题,提出一种基于IMM算法的大数据量红外图像非均匀性校正方法。通过对红外图像的灰度直方图进行双阈值映射,重新分配红外图像的灰度级,并通过自适应增强算法提高图像质量,并通过IMM算法确定图像校正时拟合响应参数状态,构建基于马尔可夫链的状态转换概率矩阵,计算红外图像信号初始状态估计与图像灰度协方差矩阵的输入交互,确定激光探测器的响应参数,校正大数据量红外图像的非均匀性。实验结果表明,所提方法图像校正的性能较好,且校正完成时间较短,校正过程较简单。
The current method to correct the infrared target image has complex correction process and the correction effect is poor. In this article,we focus on a method for correcting non-uniformity of large data volume of infrared image based on IMM algorithm. Through the gray histogram of dual-threshold mapping of infrared image,we redistributed the gray level of infrared image,and then we used the adaptive enhancement algorithm to improve the image quality. Meanwhile,we used IMM algorithm to determine the state of fitting response parameter during image correction. Moreover,we established the matrix of state transition probability based on Markov chain and calculated the Cuff chain calculates the inputting interaction between the initial state estimation of infrared image signal and the gray-scale covariance matrix of image. Finally,we determined the response parameter of laser detector. Thus,we corrected the non-uniformity of large data volume of infrared image. Simulation results show that the proposed method has good performance of image correction. Meanwhile,the completion time of correction is short and the process is simple.
The current method to correct the infrared target image has complex correction process and the correction effect is poor. In this article,we focus on a method for correcting non-uniformity of large data volume of infrared image based on IMM algorithm. Through the gray histogram of dual-threshold mapping of infrared image,we redistributed the gray level of infrared image,and then we used the adaptive enhancement algorithm to improve the image quality. Meanwhile,we used IMM algorithm to determine the state of fitting response parameter during image correction. Moreover,we established the matrix of state transition probability based on Markov chain and calculated the Cuff chain calculates the inputting interaction between the initial state estimation of infrared image signal and the gray-scale covariance matrix of image. Finally,we determined the response parameter of laser detector. Thus,we corrected the non-uniformity of large data volume of infrared image. Simulation results show that the proposed method has good performance of image correction. Meanwhile,the completion time of correction is short and the process is simple.
引文
[1]叶晶晶,杨洁.基于智能红外遥感的车库车辆自动识别引导管理系统[J].现代电子技术,2017,40(9):132-135.
[2]陈博洋.基于缓变场景的复杂神经网络非均匀性校正[J].传感技术学报,2016,29(8):1217-1221.
[3]唐艳秋,等.基于衬底温度的红外焦平面联合非均匀性校正[J].红外与激光工程,2016,45(3):33-38.
[4]孟思岐,等.一种基于恒定统计的红外图像非均匀性校正算法[J].应用光学,2017,38(2):304-308.
[5]彭勇,徐保树,张程硕.基于辐射-环温联合曲面的红外图像非均匀性校正[J].光电工程,2016,43(8):89-94.
[6]唐艳秋,等.基于衬底温度和贝叶斯估计的红外非均匀性校正[J].强激光与粒子束,2016,28(12):1-8.
[7]张丽莎,等.基于90°旋转定标和场景校正相结合的非均匀性校正技术[J].航天返回与遥感,2017,38(1):78-87.
[8]潘科辰,等.一种改进的全变分模型校正红外焦平面阵列条纹非均匀性[J].红外技术,2016,38(2):138-143.
[9]马兰,陈筱勇.对受灾区域红外图像优化识别仿真[J].计算机仿真,2017,34(3):234-237.
[10]张守荣,王华,张旭.红外焦平面阵列变积分时间下的非均匀性校正[J].激光与红外,2016,46(5):570-574.
[2]陈博洋.基于缓变场景的复杂神经网络非均匀性校正[J].传感技术学报,2016,29(8):1217-1221.
[3]唐艳秋,等.基于衬底温度的红外焦平面联合非均匀性校正[J].红外与激光工程,2016,45(3):33-38.
[4]孟思岐,等.一种基于恒定统计的红外图像非均匀性校正算法[J].应用光学,2017,38(2):304-308.
[5]彭勇,徐保树,张程硕.基于辐射-环温联合曲面的红外图像非均匀性校正[J].光电工程,2016,43(8):89-94.
[6]唐艳秋,等.基于衬底温度和贝叶斯估计的红外非均匀性校正[J].强激光与粒子束,2016,28(12):1-8.
[7]张丽莎,等.基于90°旋转定标和场景校正相结合的非均匀性校正技术[J].航天返回与遥感,2017,38(1):78-87.
[8]潘科辰,等.一种改进的全变分模型校正红外焦平面阵列条纹非均匀性[J].红外技术,2016,38(2):138-143.
[9]马兰,陈筱勇.对受灾区域红外图像优化识别仿真[J].计算机仿真,2017,34(3):234-237.
[10]张守荣,王华,张旭.红外焦平面阵列变积分时间下的非均匀性校正[J].激光与红外,2016,46(5):570-574.