Lai Rui, Yang Yin-tang, Zhou Duan, Li Yue-jin
Department of Microelectronics, Xidian University, No. 2 South Taibai Road, Xi'an, Shannxi 710071, China.
Appl Opt. 2008 Aug 20;47(24):4331-5. doi: 10.1364/ao.47.004331.
An improved scene-adaptive nonuniformity correction (NUC) algorithm for infrared focal plane arrays (IRFPAs) is proposed. This method simultaneously estimates the infrared detectors' parameters and eliminates the nonuniformity causing fixed pattern noise (FPN) by using a neural network (NN) approach. In the learning process of neuron parameter estimation, the traditional LMS algorithm is substituted with the newly presented variable step size (VSS) normalized least-mean square (NLMS) based adaptive filtering algorithm, which yields faster convergence, smaller misadjustment, and lower computational cost. In addition, a new NN structure is designed to estimate the desired target value, which promotes the calibration precision considerably. The proposed NUC method reaches high correction performance, which is validated by the experimental results quantitatively tested with a simulative testing sequence and a real infrared image sequence.
提出了一种用于红外焦平面阵列（IRFPA）的改进的场景自适应非均匀性校正（NUC）算法。该方法通过使用神经网络（NN）方法同时估计红外探测器的参数并消除导致固定模式噪声（FPN）的非均匀性。在神经元参数估计的学习过程中，传统的LMS算法被新提出的基于可变步长（VSS）归一化最小均方（NLMS）的自适应滤波算法所取代，该算法具有更快的收敛速度、更小的失调和更低的计算成本。此外，设计了一种新的神经网络结构来估计期望的目标值，这大大提高了校准精度。所提出的NUC方法具有较高的校正性能，通过使用模拟测试序列和真实红外图像序列进行定量测试的实验结果得到了验证。
