分析了传统神经网络非均匀性校正算法在空域处理过程中产生目标退化的原因, 在总结基于边缘指导的神经网络校正算法(ED-NN-NUC)与一点定标和神经网络结合的校正算法的基础上, 提出了新的组合校正算法.新算法包含预校正、粗校正和精校正三个处理模块, 利用含有弱小目标的实际红外图像进行了实验验证.结果表明, 新算法能有效地抑止目标退化, 并在运算速度上比ED-NN-NUC有一定的提高.

The cause of the target fade-out in the traditional neural network nonuniformity correction algorithms for infrared focal plane array(IRFPA)was studied. A new combinational algorithm for nonuniformity correction was proposed on the basis of analyzing the strengths and limitations of edge-directed NN scheme(ED-NN-NUC) and nonuniformity correction combining one-point calibration and NN-NUC. The new algorithm includes three modules which are pre-correction, rough correction and accurate correction. The real infrared image including dim targets was used to validate the proposed algorithm. The results show that the proposed algorithm can effectively eliminate the target fade-out, and it is less time consuming than ED-NN-NUC.