Sheng-Hui Rong, Hui-Xin Zhou, Han-Lin Qin, Rui Lai, Kun Qian
J Opt Soc Am A Opt Image Sci Vis. 2016 May 1;33(5):938-46. doi: 10.1364/JOSAA.33.000938.
In scene-based nonuniformity correction algorithms, artificial ghosting and image blurring degrade the correction quality severely. In this paper, an improved algorithm based on the diamond search block matching algorithm and the adaptive learning rate is proposed. First, accurate transform pairs between two adjacent frames are estimated by the diamond search block matching algorithm. Then, based on the error between the corresponding transform pairs, the gradient descent algorithm is applied to update correction parameters. During the process of gradient descent, the local standard deviation and a threshold are utilized to control the learning rate to avoid the accumulation of matching error. Finally, the nonuniformity correction would be realized by a linear model with updated correction parameters. The performance of the proposed algorithm is thoroughly studied with four real infrared image sequences. Experimental results indicate that the proposed algorithm can reduce the nonuniformity with less ghosting artifacts in moving areas and can also overcome the problem of image blurring in static areas.
在基于场景的非均匀性校正算法中，人为重影和图像模糊会严重降低校正质量。本文提出了一种基于菱形搜索块匹配算法和自适应学习率的改进算法。首先，通过菱形搜索块匹配算法估计两个相邻帧之间的精确变换对。然后，基于相应变换对之间的误差，应用梯度下降算法更新校正参数。在梯度下降过程中，利用局部标准差和阈值来控制学习率，以避免匹配误差的积累。最后，通过具有更新校正参数的线性模型实现非均匀性校正。利用四个真实红外图像序列对所提算法的性能进行了深入研究。实验结果表明，所提算法能够在移动区域减少非均匀性并减少重影伪像，还能克服静态区域的图像模糊问题。
