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Feedback-integrated scene cancellation scene-based nonuniformity correction algorithm

机译:基于反馈集成的场景消除基于场景的非均匀性校正算法

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摘要

A new registration-based scene-based nonuniformity correction (SBNUC) technique called the feedback-integrated scene cancellation (FiSC) method is introduced, which demonstrates an ability to correct both high- and low-spatial frequency nonuniformity (NU) in infrared focal plane arrays. The theory of scene-cancellation is further developed to include a referencing mechanism that allows spatially correlated NU to be corrected, and a practical method of application is developed. The algorithm is suitable for implementation in a real-time processing environment such as a digital signal processor. A new metric called normalized root mean-squared error for quantifying SBNUC performance is introduced and applied. When applied to real data from a cooled HgTeCd focal plane, the FiSC algorithm outperforms other SBNUC algorithms considered when provided with accurate frame-to-frame image registration. An SBNUC simulation is described and applied to several SBNUC algorithms. When the most realistic case including both high- and low-spatial frequency NU is simulated, the FiSC algorithm outperforms all others tested.
机译:引入了一种新的基于注册的基于场景的不均匀性校正(SBNUC)技术,称为反馈集成场景消除(FiSC)方法,该技术证明了在红外焦平面中校正高空间频率和低空间频率不均匀性(NU)的能力数组。场景取消理论得到了进一步发展,以包括一种可以校正与空间相关的NU的参考机制,并开发了一种实用的应用方法。该算法适合在诸如数字信号处理器之类的实时处理环境中实施。引入并应用了一种新的度量标准,用于量化SBNUC性能的归一化均方根误差。当应用于来自冷却的HgTeCd焦平面的真实数据时,FiSC算法的性能优于其他SBNUC算法(在提供准确的帧间图像配准时考虑)。描述了SBNUC仿真并将其应用于几种SBNUC算法。当模拟包括高空间频率和低空间频率NU在内的最实际情况时,FiSC算法的性能优于所有其他测试方法。

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