在红外焦平面阵列探测器（IRFPA）非均匀性校正问题中，传统的神经网络算法会出现图像边缘模糊、对比度低、“鬼影”等现象。针对此类现象，本文提出一种基于侧窗滤波改进的神经网络非均匀性校正算法。该算法首先对输入图像采用侧窗滤波获得期望图像，在去除非均匀性噪声的同时保护目标边缘细节达到提升图像质量的效果。在此基础上，通过饱和非线性函数抑制校正参数局部发散，能够有效避免校正后图像出现‘鬼影’问题。实验结果表明，使用本文提出的算法能够有效去除图像中的非均匀性噪声，且无明显“鬼影”现象。在3组测试图像序列中，平均图像粗糙度降低了30.17%。在实验计算机上连续处理400帧图像序列最大耗时为37.417 0 s，较基于双边滤波改进的对比算法耗时减少了95.05%，较基于小波主成分分析的对比算法耗时减少了45.81%。本文算法在非均匀性校正效果和算法运行效率方面具有明显优势，为小算力、低功耗移动平台实现实时非均匀性校正提供了新的研究思路。

In the nonuniformity correction problem of infrared focal plane array detector (IRFPA), the traditional neural network algorithm will appear the image edge blur, low contrast, ghosting artifacts and other phenomena. Aiming at these phenomena, this paper proposes an improved neural network nonuniformity correction algorithm based on side window filtering. The algorithm first uses side window filtering on the input image to obtain the desired image, and protects the edge details of the target while removing the non-uniform noise to improve the image quality. On this basis, it can effectively avoid the ghosting artifacts problem of the corrected image by suppressing the local divergence of the correction parameters through the saturated nonlinear function. The experimental results show that the algorithm proposed in this paper can effectively remove the non-uniform noise in the image, and there is no obvious ghosting artifacts phenomenon. The average image roughness of the three groups of test image sequences is reduced by 30.17%. The maximum time consumption for continuous processing of 400 image sequences on the experimental computer is 37.417 0 s, which is 95.05% less than that of the comparison algorithm improved based on bilateral filtering, and 45.81% less than that of the comparison algorithm based on wavelet principal component analysis. The algorithm in this paper has obvious advantages in nonuniformity correction effect and algorithm operation efficiency, which provides a new research idea for real-time nonuniformity correction on mobile platforms with small computational power and low power consumption.