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卡尔曼滤波算法在迭代过程收敛之前无法有效抑制红外图像的非均匀性噪声。因其收敛速度慢,严重影响了图像的整体校正效果。由于迭代过程的收敛速度与初始状态参数密切相关,提出了一种采用优化的初始状态参数的改进卡尔曼滤波算法。优化的初始状态参数通过对用于两点校正的校正参数进行转换并对其结果进行统计得到。对于真实的红外图像序列实验结果显示,传统卡尔曼滤波算法在经过数次迭代之后才进入收敛状态,改进该算法必须从一开始就已经接近收敛状态。实验结果表明:与传统算法相比,改进算法收敛速度快,对起始段图像序列的非均匀校正效果有明显改善。
The Kalman filter algorithm can not effectively suppress the nonuniform noise of the infrared image until the iterative process converges. Because of its slow convergence, seriously affecting the overall image correction. Because the convergence speed of the iterative process is closely related to the initial state parameters, an improved Kalman filter algorithm is proposed with the optimized initial state parameters. The optimized initial state parameters are obtained by converting the calibration parameters used for the two-point calibration and the result is statistically calculated. Experimental results on real infrared image sequences show that the traditional Kalman filter algorithm does not enter the convergence state after several iterations, so the improved algorithm must be close to the convergence state from the very beginning. The experimental results show that compared with the traditional algorithm, the improved algorithm has a faster convergence rate and significantly improves the non-uniform correction of the initial image sequence.