An exposure fusion approach without ghost for dynamic scenes

Chunmeng Wang, Changhe Tu
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引用次数: 10

Abstract

In this paper, we present a new method for multi-exposure images fusion of dynamic scenes to remove ghost artefacts. First we unify the exposure level of each input image to that of the reference image and generate ghost detection binary images using difference method with an adaptive threshold strategy. Then we modify the binary images further with morphological operations. Finally, we redefine weight maps of exposure fusion according to the modified binary images to obtain the fused image. We can generate different deghosting result base on an optional reference image. Our method achieves high-quality deghosting effect even if the reference image is not well exposed. Besides, our method can process the misaligned exposures caused by camera shake effectively. Experimental results of various dynamic scenes prove the improvement over previous reference-based deghosting methods.
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一种无鬼影的动态场景曝光融合方法
本文提出了一种新的动态场景多曝光图像融合去除伪影的方法。首先,我们将每个输入图像的曝光水平与参考图像的曝光水平统一,并使用自适应阈值策略的差分方法生成鬼检测二值图像。然后对二值图像进行形态学处理。最后,根据修改后的二值图像重新定义曝光融合的权重图,得到融合后的图像。我们可以根据可选的参考图像生成不同的消影结果。我们的方法即使在参考图像曝光不佳的情况下也能达到高质量的去鬼影效果。此外,该方法还能有效地处理由相机抖动引起的不对准曝光。各种动态场景的实验结果证明了该方法比以往基于参考的去噪方法的改进。
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