一种基于核回归插值的自适应非均匀性校正算法

针对基于神经网络的场景自适应非均匀性校正(Non-Uniformity Correction, NUC)算法在消除红外成像系统输出图像噪声时容易产生的“鬼影”现象,提出了一种改进的自适应非均匀性校正算法,将核回归插值技术应用到神经网络算法中,有效降低了自适应非均匀性算法产生“鬼影”现象的概率。实验结果表明,与传统的神经网络算法相比,本文算法在相同条件下既能有效消除非均匀噪声,又能大大抑制“鬼影”现象的产生。

An Adaptive Non-uniformity Correction Algorithm Based on Kernel Regression Interpolation

Since the scene adaptive nonuniformity correction (NUC) algorithm based on neural network is easy to generate the phenomenon of "ghost" when it removes the noise in the images output by infrared imaging systems, an improved adaptive nonuniformity correction algorithm is proposed. By applying kernel regression interpolation to the neural network algorithm, the probability of "ghost" phenomenon caused by the adaptive nonuniformity algorithm is reduced effectively. The experimental results show that compared with the traditional neural network algorithm, the proposed algorithm not only can eliminate nonuniformity noise effectively, but also can restrain the generation of "ghost" phenomenon greatly under the same conditions.