时空域自适应滤波非均匀性校正算法  

Space-Time Domain Adaptive Filtering Non-uniformity Correction Algorithm

作　　者：郭玉婷 贾晓洪[1,2] 李丽娟 刘俊明[1,2] GUO Yuting;JIA Xiaohong;LI Lijuan;LIU Junming(China Air-to-Air Missile Research Institute,Luoyang 471009,China;Aviation Key Laboratory of Science and Technology on Airborne Guide Weapons,Luoyang 471009,China)

机构地区：[1]中国空空导弹研究院,河南洛阳471009 [2]航空制导武器航空科技重点实验室,河南洛阳471009

出　　处：《红外技术》2023年第5期482-487,共6页Infrared Technology

摘　　要：由于红外焦平面探测器受到制造工艺等限制,图像不可避免地会存在非均匀性。传统神经网络算法会留下“鬼影”的问题,本文改进传统神经网络算法,利用引导滤波图像作为期望模板,防止图像的边缘被滤波器平滑。当场景运动时,通过时域迭代的策略来不断进行非均匀性校正参数的更新。为了抑制算法中常见的鬼影现象,设计了基于空域局部方差和时域场景变化率相结合的自适应学习率,利用前后的校正参数自适应调整阈值。实验仿真表明,本文所提的算法相比于传统算法均方根误差下降45.45%左右,可以在校正图像非均匀性的同时很好地抑制“鬼影”现象。Because the infrared focal plane detector is limited by manufacturing technology,the image is inevitably nonuniform.The traditional neural network algorithm solves the"ghost"problem using the guided filtering image as the expected template to prevent image edge smoothing by the filter.When the scene is moving,the nonuniformity correction parameters are continuously updated using the time-domain iteration strategy.To suppress the common ghosting phenomenon in the algorithm,an adaptive learning rate was designed based on a combination of the spatial local variance and the time-domain scene change rate,and the threshold was adjusted adaptively using the correction parameters before and after.Simulation results show that the root mean square error of the proposed algorithm is reduced by 45.45%compared with that of the traditional algorithm,and the proposed algorithm can suppress the"ghost"phenomenon well while correcting image nonuniformity.

关 键 词：红外焦平面 非均匀性校正 基于场景 神经网络 引导滤波 

分 类 号：TJ761.3[兵器科学与技术—武器系统与运用工程]