Hardie Russell C, Baxley Frank, Brys Brandon, Hytla Patrick
Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45459-0232, USA.
Opt Express. 2009 Aug 17;17(17):14918-33. doi: 10.1364/oe.17.014918.
In this paper, we present a scene-based nouniformity correction (NUC) method using a modified adaptive least mean square (LMS) algorithm with a novel gating operation on the updates. The gating is designed to significantly reduce ghosting artifacts produced by many scene-based NUC algorithms by halting updates when temporal variation is lacking. We define the algorithm and present a number of experimental results to demonstrate the efficacy of the proposed method in comparison to several previously published methods including other LMS and constant statistics based methods. The experimental results include simulated imagery and a real infrared image sequence. We show that the proposed method significantly reduces ghosting artifacts, but has a slightly longer convergence time.
在本文中，我们提出了一种基于场景的非均匀性校正（NUC）方法，该方法使用了一种改进的自适应最小均方（LMS）算法，并在更新过程中采用了一种新颖的门控操作。该门控操作旨在通过在缺乏时间变化时停止更新，显著减少许多基于场景的NUC算法产生的重影伪像。我们定义了该算法，并给出了一些实验结果，以证明所提方法与几种先前发表的方法（包括其他基于LMS和恒定统计量的方法）相比的有效性。实验结果包括模拟图像和真实红外图像序列。我们表明，所提方法显著减少了重影伪像，但收敛时间略长。
