针对传统的基于场景的红外焦平面阵列非均匀性校正算法收敛速度慢和校正精度不高的缺点，提出了一种基于扩展全变分的红外焦平面阵列非均匀性校正方法。在分析全变分算法的图像去噪性能的基础上，针对运动的红外图像序列，扩展了全变分的应用范围。通过最小化非均匀校正后图像的全变分，利用最陡下降法，得到计算增益量校正因子和偏移量校正因子的迭代公式。针对校正图像存在的鬼影现象，设计了一种自适应阈值控制的鬼影消除方法。实验表明：相较于目前已有的方法，该方法有效地去除了原始红外图像的固定图案噪声，较大程度地保留了图像细节信息，提高了图像质量。

Traditional scene-based nonuniformity correction algorithms for infrared focal plane array suffer from two major drawbacks: slow convergence rate and low correction accuracy. Aiming at these problems, a new method based on extended total variation is proposed to correct the nonuniformity for infrared focal plane arrays. Based on the analysis of total variation-based de-noising performance, the scope of total variation is extended for motional infrared image sequences. By minimizing the total variation of corrected images, iterative formula to compute the gain and offset factors is obtained using steepest descent method. For eliminating the ghosting effect, an adaptive threshold method is designed. The experiments show that compared with the existing methods, this method can effectively remove the noise in original infrared images and largely retain image information at the same time. The image quality is therefore improved.