For infrared focal-plane array imaging system,scene-based non-uniformity correction is key technique to deal with fixed pattern noise.Existing algorithms are mainly restricted by convergence speed and ghosting artifacts.In this paper,a novel adaptive scene-based non-uniformity correction technique is presented,which is based on constant-statistics method (CS).Utilizing temporal statistics of infrared image sequences,the proposed method applies an alphatrimmed mean filter to estimate detector parameters and minimize sample asymptotic variance estimate.Performance of proposed technique is evaluated by simulation and real non-uniformity image.Experimental results show the proposed method inherits characteristics of fast convergence of CS method and increases peak signal to noise ratio by 44.5％ and 32.9％ respectively,and image ghost problem is improved obviously.%对红外焦平面阵列成像系统而言,基于场景的非均匀校正技术是处理固定图案噪声的关键技术.现有的非均匀校正算法主要被收敛速度和鬼像问题所限制.提出一种新的基于恒定统计算法的自适应场景非均匀校正技术.利用红外图像序列的时域统计信息结合提出的α修正均值滤波来估计探测器的参数,通过减少样本的渐进方差估计,完成成像系统的非均匀性校正.通过模拟和真实的非均匀性图像对算法的性能进行评价.实验结果表明,在继承恒定统计算法快速收敛的同时,图像峰值信噪比较恒定校正法及常系数α校正算法分别有44.5％和32.9％的提升,图像鬼像问题有明显改善.
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