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Aligning multi-exposed images: What are the good feature and similarity measure?

机译:对齐多重曝光的图像:有哪些好的功能和相似性度量?

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High dynamic range (HDR) imaging, a technique to synthesize a sequence of multi-exposed images, has been recently developed to reduce the dynamic range gap between captured images and real scenes. Unlike conventional cases of varying illumination in which each image is best exposed, the multi-exposed images contain severely under/over-exposed regions and have significant variations in intensity, which offer great challenge in image registration. This paper aims to identify what is the invariant representation of multi-exposed images and which similarity measure is good for these images. To this end, we present a comprehensive comparison of existing ordering features and similarity measures. Experimental results show that the mutual information is the best similarity metric, and the median threshold bitmap is the best feature in terms of accuracy and robustness.
机译:高动态范围(HDR)成像是一种合成一系列多重曝光图像的技术,最近已经开发出来,以减小捕获的图像和真实场景之间的动态范围差距。与其中每个图像被最佳曝光的变化照明的常规情况不同,多重曝光的图像包含严重不足/过度曝光的区域,并且强度有显着变化,这在图像配准中提出了很大的挑战。本文旨在确定什么是多次曝光图像的不变表示,以及哪种相似性度量适合这些图像。为此,我们将对现有订购功能和相似性度量进行全面比较。实验结果表明,在准确性和鲁棒性方面,互信息是最好的相似性度量,而中值阈值位图是最好的特征。

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