基于高阶统计量的红外焦平面非均匀校正算法

High-order-statistics based nonuniformity correction for infrared focal plane arrays

根据高斯噪声高于二阶累积量为零的特性,提出基于高阶统计量的红外焦平面非均匀校正算法,可从图像场景中自适应估计阵列单元的增益和偏置参数.提出的算法包含两个部分,第一部分利用统计矩和累积量周期估计红外焦平面的模型参数,第二部分利用维纳滤波恢复真实图像.仿真图像序列表明这种算法有效降低了固定图案噪声,达到了较高的非均匀校正水平.

According to the property that for Gaussian signals only, all cumulant spectra of order greater than two are identically zero, a High-Order-Statistics based Nonuniformity Correction（HOS-NUC） approach is taken for the adaptive estimation of the gain and bias nonuniformity in infrared focal-plane array sensors from scene data. The proposed algorithm consists of two main parts： the first part involves a periodic statistical estimation of the model parameters using the moment and cumulant; the second part involves the estimated parameters in restoring the true image by a winner filter. The proposed method has been tested with video sequences of simulated infrared data, reducing the fixed pattern noise and reaching high correction levels.