为了解决传统神经网络算法在用于红外焦平面阵列(Infrared Focal Plane Array,IRFPA)非均匀性校正(Non-Uniformity Correction,NUC)时所面临的边缘模糊、收敛速度慢等问题,通过引入图像局部梯度特性对该算法进行了改进。通过用局部梯度相似度信息构造权值函数来对区域进行加权滤波,可以保留图像边缘信息。在迭代运算中,将梯度幅值加权的自适应参数规整因子加入了误差损失函数,并引入梯度幅值相关的自适应步长用以代替传统的固定步长,从而进一步提升了算法的校正效果和收敛速度。然后对算法的性能曲线和校正结果进行了分析。结果表明,与传统算法相比,改进的神经网络校正算法取得了更好的校正效果,其校正误差稳定低于前者,实现了有效抑制边缘模糊和提升收敛速度的目标。

To solve the problems of edge blurring and slow convergence speed faced when a traditional neural network method is used in the non-uniformity correction of an infrared focal plane array, the traditional neural network algorithm is improved by introducing the local gradient characteristics of images. By using the weighting function constructed with local gradient similarity information to weight a region, the image edge information can be preserved. In iterative computation, an adaptive weighting factor with gradient amplitude is added to the error loss function, and the adaptive step size associated with the gradient amplitude is introduced to replace the traditional fixed step size. Thus, the correction effect and convergence speed of the algorithm are further improved. Then, the performance curve of the algorithm and its correction result are analyzed. The result shows that the improved neural network correction algorithm has achieved better non-uniformity correction effect than the traditional algorithm. Its correction error is less than that of the traditional method. The object to effectively suppress edge blur and improve convergence speed is realized.