摘要
提出一种实时校正方法,在非均匀性校正的过程中根据当前场景信息自适应调整积分时间.首先获取不同积分时间下的背景噪音并进行存储,根据特定的阈值控制积分时间的切换;进而将探测器输出减去相应积分时间下的背景噪音后作为神经网络的输入,从而不断地更新校正参量.这样既能有效弥补神经网络校正算法在低频噪音占优时的不足,降低由于积分时间改变引起的非均匀性,又能够补偿系统的温漂.对真实的红外图像序列实验表明,文中提出的算法在积分时间切换的同时可以得到合适的校正参量,并保证校正后的图像质量,能够实现实时校正.
The real-time correction algorithm is proposed, which adjusts the integration time adaptively according to the current scene information in the process of correction. Firstly, the background images are stored at different integration time and the integration time is adjusted by the specific thresholds. Then the corresponding background image is subtracted from the output of IRPPA and the result are taken as the input of neural network. So it can make up the deficiency of neural network, and reduce the non-uniformity caused by the change of integration time. Experiments of real IRFPA videos show that the proposed algorithm insures high quality of image, also has the advantages of integration time adjusting adaptively and real-time correction.
出处
《光子学报》
EI
CAS
CSCD
北大核心
2013年第4期486-490,共5页
Acta Photonica Sinica
关键词
红外焦平面
神经网络
积分时间
Infrared focal plane arrays Neural network Integration time
