CAJ | 학술논문

提出了一种结合图像匹配和神经网络算法的焦平面阵列非均匀性校正算法.算法首先用最新的校正系数对图像进行非均匀性校正,输出校正结果;然后对相邻两帧图像进行匹配,估计出相邻帧之间图像的运动量;最后用神经网络算法分别对校正系数进行正向和反向自适应更新.采用图像匹配技术保证了校正系数更新时不会引起场景的模糊,采用校正系数双向更新策略可以保证每帧都能对每个像元的系数至少进行一次更新,与常用的神经网络校正算法相比,降低了对场景统计特性的要求,收敛速度较快.使用模拟添加噪声和采集的红外图像序列对算法进行仿真验证,结果表明,给出的算法校正效果优常用的神经网络非均匀性校正算法.
제출료일충결합도상필배화신경망락산법적초평면진렬비균균성교정산법.산법수선용최신적교정계수대도상진행비균균성교정,수출교정결과;연후대상린량정도상진행필배,고계출상린정지간도상적운동량;최후용신경망락산법분별대교정계수진행정향화반향자괄응경신.채용도상필배기술보증료교정계수경신시불회인기장경적모호,채용교정계수쌍향경신책략가이보증매정도능대매개상원적계수지소진행일차경신,여상용적신경망락교정산법상비,강저료대장경통계특성적요구,수렴속도교쾌.사용모의첨가조성화채집적홍외도상서렬대산법진행방진험증,결과표명,급출적산법교정효과우상용적신경망락비균균성교정산법.
An improved nonuniformity correction (NUC) algorithm combining image matching and neural network(NN) for infrared focal plane array sensors was presented. Firstly, nonuniformity of the FPA response was removed by NUC compensation. Then, motion parameters of the image were estimated by matching pairs of image frames. Finally, coefficients were adaptively updated according to bidirectional-renew strategy based on neural network. Image matching technique could effectively avoid faintness when coefficients were updating. Additionally, the bidirectional-renew strategy was used to guarantee coefficients of each pixel be calculated at least once when new image frame came. The new algorithm used image matching technique to get scene motion information, and used neural network for coefficients bidirectional-renew strategy. It had a lower statistical overhead on scenes and approached convergence more quickly than the often used neural network based NUC algorithms. A theoretical analysis was performed on a collection of infrared image frames to study the accuracy of the new NUC algorithm. It proves that it has higher-quality correction ability than simple neural network based NUC algorithm.