This paper proposes a hybrid synthesis method for multi-exposure image fusion taken by hand-held cameras. Motions either due to the shaky camera or caused by dynamic scenes should be compensated before any content fusion. Any misalignment can easily cause blurring/ghosting artifacts in the fused result. Our hybrid method can deal with such motions and maintain the exposure information of each input effectively. In particular, the proposed method first applies optical flow for a coarse registration, which performs well with complex non-rigid motion but produces deformations at regions with missing correspondences. The absence of correspondences is due to the occlusions of scene parallax or the moving contents. To correct such error registration, we segment images into superpixels and identify problematic alignments based on each superpixel, which is further aligned by PatchMatch. The method combines the efficiency of optical flow and the accuracy of PatchMatch. After PatchMatch correction, we obtain a fully aligned image stack that facilitates a high-quality fusion that is free from blurring/ghosting artifacts. We compare our method with existing fusion algorithms on various challenging examples, including the static/dynamic, the indoor/outdoor and the daytime/nighttime scenes. Experiment results demonstrate the effectiveness and robustness of our method.

翻译：本文提出了一种用于手持相机多曝光图像融合的混合合成方法。由相机抖动或动态场景引起的运动必须在任何内容融合前进行补偿。任何未对齐都容易在融合结果中产生模糊/鬼影伪影。我们的混合方法能够处理此类运动并有效保留每张输入的曝光信息。具体而言，该方法首先应用光流进行粗略配准，该方案在复杂非刚性运动场景下表现良好，但在对应缺失区域会产生变形。对应缺失源于场景视差遮挡或运动内容。为修正此类错误配准，我们将图像分割为超像素，基于每个超像素识别问题对齐区域，并进一步通过PatchMatch进行对齐。该方法结合了光流的高效性和PatchMatch的精确性。经过PatchMatch修正后，我们获得完全对齐的图像堆栈，从而支持高质量的无模糊/鬼影伪影融合。我们将本方法与现有融合算法在包括静态/动态、室内/室外、白天/夜晚等多种挑战性场景下进行对比。实验结果验证了本方法的有效性和鲁棒性。