基于光流和PatchMatch的手持相机输入曝光融合
Exposure Fusion for Hand-held Camera Inputs with Optical Flow and PatchMatch
AI总结：
本文提出一种混合多曝光图像融合方法，结合光流粗配准与PatchMatch超像素级校正，有效解决手持相机抖动和动态场景导致的未对齐问题，实现无模糊和重影伪影的高质量融合。
摘要：
本文提出了一种用于手持相机拍摄的多曝光图像融合的混合合成方法。在进行任何内容融合之前，应补偿由相机抖动或动态场景引起的运动。任何未对齐都很容易在融合结果中导致模糊/重影伪影。我们的混合方法能够处理此类运动，并有效保持每个输入的曝光信息。具体而言，所提出的方法首先应用光流进行粗配准，该方法对复杂的非刚性运动表现良好，但会在缺失对应关系的区域产生变形。对应关系的缺失是由于场景视差或移动内容的遮挡造成的。为了纠正这种错误的配准，我们将图像分割为超像素，并基于每个超像素识别有问题的对齐，然后通过PatchMatch进一步对齐。该方法结合了光流的效率和PatchMatch的准确性。经过PatchMatch校正后，我们获得了完全对齐的图像堆栈，从而实现了无模糊/重影伪影的高质量融合。我们在各种具有挑战性的示例上将我们的方法与现有的融合算法进行了比较，包括静态/动态、室内/室外以及白天/夜间场景。实验结果证明了我们方法的有效性和鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
