Patch-Based correlation for deghosting in exposure fusion
Patch-Based correlation for deghosting in exposure fusion
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DOI：
10.1016/j.ins.2017.05.019
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Wei Zhang;Shengnan Hu;Kang Liu
中科院分区：
文献类型：
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作者：
Wei Zhang;Shengnan Hu;Kang Liu
提出了一种鲁棒的曝光融合算法来解决动态场景中的运动去除和细节保持问题。使用一次曝光作为参考，该算法通过比较图像的结构一致性来检测曝光堆栈中的运动，所述图像的结构一致性由每个图像的块与参考的对应块之间的线性相关度来测量。然后，在运动去除之后，合成具有一致内容的一堆潜像。为了保持细节，引入对比度准则来衡量曝光质量并生成每个潜像的可见性图。在可见性图的指导下，通过无缝合并潜像来产生色调映射的HDR图像，该图像没有重影并且保留了所有细节。各种动态场景的曝光融合测试表明，该方法优于现有的国家的最先进的方法。
We present a robust exposure fusion algorithm to tackle the problems of motion removal and detail preserving in dynamic scenes. Using one exposure as a reference, the algorithm detects motion in an exposure stack by comparing the structural consistency of images as measured by the degree of linear correlation between patches of each image and the corresponding patches of the reference. Then, after motion removal, a stack of latent images with consistent content is synthesized. For detail preserving, a contrast criterion is introduced to measure the quality of exposure and generate visibility maps of each latent image. Guided by the visibility maps, a tonemapped-like HDR image, which is ghost-free and has all details preserved, is produced by seamlessly merging the latent images. Exposure fusion tests on various dynamic scenes demonstrate the superiority of the proposed method over existing state-of-the-art approaches.