Exposure fusion often suffers from ghost artifacts caused by object movement when capturing a dynamic scene. In this paper, two types of consistency concepts are introduced to enforce the guidance of reference image for motion detection and ghost removal. Specifically, the inter-consistency, representing the similarity of pixel intensities among different exposures, is weak due to different exposure settings. Histogram matching is employed to recover the inter-consistency, so motion can be detected easily as the pixel differences are mostly caused by content changes due to object movement. To further restrain the outlier weights in fusion, motion detection is performed in super-pixel level to ensure that pixels with similar intensities and structure share similar fusion weights, which is referred as intra-consistency. Experiments in various dynamic scenes show that the proposed algorithm can find the motion more effectively than existing methods and produce pleasant fusion results free of ghosting artifacts.
Wei Zhang et al. Patch-Based Correlation for Deghosting in Exposure Fusion, Information Sciences. 2017.
Wei Zhang et al. Motion-free Exposure Fusion based on Inter-consistency and Intra-consistency. Information Sciences. Nov. 2016.
Wei Zhang et al. Exploiting Patch-based Correlation for Ghost Removal in Exposure Fusion, Proc. of International Conference on Multimedia & Expo (ICME), 2017.
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