An Improved Scene-based Nonuniformity Correction Algorithm for Infrared Focal Plane Arrays Using Neural Networks  

作　　者：隋婧 金伟其 董立泉 王霞 郭宏 

机构地区：[1]School of Information Science and Technology,Beijing Institute of Technology,Beijing 100081,China

出　　处：《Defence Technology（防务技术）》2006年第2期117-122,共6页Defence Technology

基　　金：SponsoredbySponsoredbytheTenth-FiveNationalDefensePre-researchFoundationofChina(30404)

摘　　要：The improved scene-based adaptive nonuniformity correction (NUC) algorithms using a neural network (NNT) approach for infrared image sequences are presented and analyzed. The retina-like neural networks using steepest descent model was the first proposed infrared focal plane arrays (IRFPA) nonuniformity compensation method,which can perform parameter estimation of the sensors over time on a frame by frame basis. To increase the strength and the robustness of the NNT algorithm and to avoid the presence of ghosting artifacts,some optimization techniques,including momentum term,regularization factor and adaptive learning rate,were executed in the parameter learning process. In this paper,the local median filtering result of AX^U_ ij (n) is proposed as an alternative value of desired network output of neuron X_ ij (n),denoted as T_ ij (n),which is the local spatial average of AX^U_ ij (n) in traditional NNT methods. Noticeably,the NUC algorithm is inter-frame adaptive in nature and does not rely on any statistical assumptions on the scene data in the image sequence. Applications of this algorithm to the simulated video sequences and real infrared data taken with PV320 show that the correction results of image sequence are better than that of using original NNT approach,especially for the short-time image sequences (several hundred frames) subjected to the dense impulse noises with a number of dead or saturated pixels.The improved scene-based adaptive nonuniformity correction （NUC） algorithms using a neural network （NNT） approach for infrared image sequences are presented and analyzed. The retlna-like neural networks using steepest descent model was the first proposed infrared focal plane arrays （IRFPA） nonuniformity compensation method, which can perform parameter estimation of the sensors over time on a frame by frame basis. To increase the strength and the robustness of the NNT algorithm and to avoid the presence of ghosting artifacts, some optimization techniques, including momentum term, regularization factor and adaptive learning rate, were executed in the parameter learning process. In this paper, the local median filtering result of Xij （ n ） is proposed as an alternative value of desired network output of neuron Xij （ n ）, denoted as Tij （ n ）, which is the local spatial average of Xij （ n ） in traditional NNT methods. Noticeably, the NUC algorithm is inter-frame adaptive in nature and does not rely on any statistical assumptions on the scene data in the image sequence. Applications of this algorithm to the simulated video sequences and real infrared data taken with PV320 show that the correction results of image sequence are better than that of using original NNT approach, especially for the short-time image sequences （several hundred frames） subjected to the dense impulse noises with a number of dead or saturated pixels.

关 键 词：红外线 焦面位移排列 神经系统 图像系统 光化学 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]