多本底采样自适应非均匀校正算法  

Multiple Background Sampling Adaptive Non-Uniform Correction Algorithm

作　　者：段程鹏[1] 刘伟[1] 陈耀弘 谢庆胜[1] 易波[1] 周祚峰[1] 

机构地区：[1]中国科学院西安光学精密机械研究所,陕西西安710119

出　　处：《光学学报》2016年第10期202-208,共7页Acta Optica Sinica

基　　金：国家自然科学基金(61201376)

摘　　要：对于红外输出图像的非均匀性,通常可采用两点校正法和神经网络法进行改善。但两点校正法不能有效地克服环境温度漂移的影响;神经网络法收敛缓慢,使静止图像逐渐融入背景,导致运动目标出现伪像。提出一种多本底采样自适应非均匀校正算法,在不同的环境温度点采集多组高低温本底,根据最小二乘法拟合计算得到非均匀校正系数和环境温度的关系,根据环境温度的改变自适应完成非均匀校正。测试结果表明,该方法简单可行,能够较好地克服环境温度漂移的影响。To improve the non-uniformity of infrared output images, the two-point correction and neural network algorithms are commonly used. However, the two-point correction algorithm cannot overcome the influence of environmental temperature drift effectively. Due to the slow convergence speed of the neural network algorithm, the still images using the neural network algorithm gradually integrate to the background, and the moving target appears an artifact. So a multiple background sampling adaptive non-uniform correction algorithm is proposed, and multiple groups of high- and low-temperature backgrounds are collected at different temperature points. The relationship between the achieved non-uniform correction coefficient and the environmental temperature is fitted by means of the least square method, and the adaptive non-uniform correction is implemented based the change of the environmental temperature. Test results show that this method is simple and feasible, and it can effectively overcome the influence of environmental temperature drift.

关 键 词：光计算 非均匀校正 神经网络法 多本底采样 最小二乘法 

分 类 号：TN215[电子电信—物理电子学]