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

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.

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

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 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.