适应积分时间调整的红外图像非均匀性校正方法

针对应用常规红外图像非均匀性校正方法在变积分时间时,图像灰度值会发生改变的现象,提出了一种适应积分时间调整的红外图像非均匀性校正方法.该方法将不同积分时间、不同温度的黑体定标数据和对应的理论红外辐射量整合为一个整体数据库,借助神经网络损失函数和误差反向传递机制,对模型中的校正系数进行学习.训练得到的校正网络能在红外相机积分时间实时调整过程中,保证图像均匀地稳定输出,对后端红外图像处理有着重要意义,并验证训练该网络不需要大量定标数据.而针对红外探测器响应漂移的现象,则提出了在线修正校正系数的方法以有效应对.

Infrared Image Nonuniformity Correction Method Adapted to Adjustment of Integration Time

For the application of conventional infrared image non-uniformity correction method,when the integration time is adjusted,the image gray level may change accordingly,an infrared image nonuniformity correction method adapted to adjustment of integration time was proposed.In this method,the black-body calibration images with different integration time and temperature,and the corresponding theoretical infrared radiation were integrated into a whole database,with the loss function of neural network and error reverse transfer mechanism,the correction coefficients in the model were learned during the training.The trained network correct non-uniformity can ensure the stable output of images during the real-time adjustment of infrared camera integration time,it is significant for post infrared image processing.And it is proved that the training of this network does not need enormous calibration datum.In order to deal with the problem of infrared detector response drift,an online learning correction coefficient method is proposed.