飞行器光学成像与测量技术研究室知识库

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. © 2016, Chinese Lasers Press. All right reserved.