Non-uniformity correction (NUC) plays an important role in improving image quality of the infrared focal plane array(IRFPA) .A non-uniformity correction algorithm for IRFPA based on virtual perimeter diaphragm strips is proposed. This algorithm initially executes one-point correction to the perimeter detectors using artficial neural networks. Then based on the scene information and shift between adjacent frames, a special algebraic algorithm is proposed to transport correction parameters from perimeter detectors to those interior un-corrected ones. In this way, the correction parameters of the whole field of view (FOV) are calculated.It doesn＇t need to change the mechanical structure of the imaging system. This novel algorithm has a high computational efficiency and is able to realize adapfive one-point correction without covering the central FOV rapidly. It proves to get a satisfying correction effect after being tested by using real and simulated infrared images.%非均匀性校正是提高红外焦平面阵列成像质量的关键环节.本文提出了一种基于虚拟边框视场光阑的红外非均匀性校正算法.该算法用人工神经网络对边框像元进行初始校正,形成校正虚拟边框,再根据场景信息和帧间位移,将偏置校正参数逐行逐列传递,可消除焦平面阵列全视场响应的偏置非均匀性.由于算法主要基于代数运算,运算量较低,故能根据场景信息自适应地实现快速、高效的一点校正;且不需要对成像系统进行机械结构改造,与传统代数算法相比,适应性更强.真实红外图像与仿真图像对算法的检验结果,证明了方法的有效性.
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