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...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.展开更多
For infrared focal plane graded during signal acquisition array sensors, imagery is departicularly nonuniformity. In this paper, an adaptive nonuniformity correction technique is proposed which simultaneously estimate...For infrared focal plane graded during signal acquisition array sensors, imagery is departicularly nonuniformity. In this paper, an adaptive nonuniformity correction technique is proposed which simultaneously estimates detector-level and readout- channel-level correction parameters using neural network approaches. Firstly, an improved neural network framework is designed to compute the desired output. Secondly, an adaptive learning rate rule is used in the gain and offset parameter estimation process. Experimental results show the proposed algorithm can achieve a faster convergence speed and better stability, remove nonuniformity and track parameters drift effectively, and present a good adaptability to scene changes and nonuniformity conditions.展开更多
该文旨在改进风速订正模型,以提高第6代跨学科气候研究模式(Model for Interdisciplinary Research on Climate Version 6,MIROC6)历史时期10 m风速的模拟准确性。研究基于Informer模型,结合多层感知机,构造了非平稳Informer(Ns-Informe...该文旨在改进风速订正模型,以提高第6代跨学科气候研究模式(Model for Interdisciplinary Research on Climate Version 6,MIROC6)历史时期10 m风速的模拟准确性。研究基于Informer模型,结合多层感知机,构造了非平稳Informer(Ns-Informer)10 m风速订正模型。研究提出了一种新的加权趋势均方误差损失函数,以优化模型在高风速条件下的订正性能,选取北京站、拐子湖站、茫崖站、吉安站4个代表站进行验证。结果表明:Ns-Informer在月尺度和年代际尺度上均能还原风速时间分布特征,订正后10 m风速的均方根误差降低20%~50%,在风速超过5 m·s^(-1)时表现最佳。Ns-Informer订正后的月平均10 m风速演变趋势与观测吻合度提高。在夏季和秋季订正效果显著,月平均10 m风速均方根误差降低25%以上。年代际变化趋势的订正表明Ns-Informer能矫正MIROC6对风速长期变化趋势的偏差,订正后的风速序列捕获了不同站点风速长期的上升或下降趋势。未来情景检验进一步表明:Ns-Informer能在SSP1-2.6情景下对高风速阈值的订正稳定性优于MIROC6。Ns-Informer可以有效降低MIROC6的系统偏差,为未来气候变化情景下风速的精确预估提供参考。展开更多
文摘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.
基金supported by the National Natural Science Foundation of China (61101199)the Natural Science Foundation of Jiangsu Province (K2011699)the Colleges and Universities Innovation Projects (CX08B 045Z)
文摘For infrared focal plane graded during signal acquisition array sensors, imagery is departicularly nonuniformity. In this paper, an adaptive nonuniformity correction technique is proposed which simultaneously estimates detector-level and readout- channel-level correction parameters using neural network approaches. Firstly, an improved neural network framework is designed to compute the desired output. Secondly, an adaptive learning rate rule is used in the gain and offset parameter estimation process. Experimental results show the proposed algorithm can achieve a faster convergence speed and better stability, remove nonuniformity and track parameters drift effectively, and present a good adaptability to scene changes and nonuniformity conditions.
文摘该文旨在改进风速订正模型,以提高第6代跨学科气候研究模式(Model for Interdisciplinary Research on Climate Version 6,MIROC6)历史时期10 m风速的模拟准确性。研究基于Informer模型,结合多层感知机,构造了非平稳Informer(Ns-Informer)10 m风速订正模型。研究提出了一种新的加权趋势均方误差损失函数,以优化模型在高风速条件下的订正性能,选取北京站、拐子湖站、茫崖站、吉安站4个代表站进行验证。结果表明:Ns-Informer在月尺度和年代际尺度上均能还原风速时间分布特征,订正后10 m风速的均方根误差降低20%~50%,在风速超过5 m·s^(-1)时表现最佳。Ns-Informer订正后的月平均10 m风速演变趋势与观测吻合度提高。在夏季和秋季订正效果显著,月平均10 m风速均方根误差降低25%以上。年代际变化趋势的订正表明Ns-Informer能矫正MIROC6对风速长期变化趋势的偏差,订正后的风速序列捕获了不同站点风速长期的上升或下降趋势。未来情景检验进一步表明:Ns-Informer能在SSP1-2.6情景下对高风速阈值的订正稳定性优于MIROC6。Ns-Informer可以有效降低MIROC6的系统偏差,为未来气候变化情景下风速的精确预估提供参考。
