The paper studies the image fusion from multiple images taken by hand-held cameras with different exposures. Existing methods often generate unsatisfactory results, such as blurring/ghosting artifacts due to the problematic handling of camera motions, dynamic contents, and inappropriately fusion of local regions (e.g., over or under exposed). In addition, they often require a high-quality image registration, which is hard to achieve in scenarios with large depth variations and dynamic textures, and is also time-consuming. In this paper, we propose to enable a rough registration by a single homography and combine the inputs seamlessly to hide any possible misalignment. Specifically, the method first uses a Markov Random Field (MRF) energy for the labeling of all pixels, which assigns different labels to different aligned input images. During the labeling, it chooses well-exposured regions and skips moving objects at the same time. Then, the proposed method combines a Laplacian image according to the labels and constructs the fusion result by solving the Poisson equation. Furthermore, it adds some internal constraints when solving the Poisson equation for balancing and improving fusion results. We present various challenging examples, including static/dynamic, indoor/outdoor and daytime/nighttime scenes, to demonstrate the effectiveness and practicability of the proposed method.

本文研究了不同曝光度的手持相机拍摄的多个图像的图像融合。现有的方法通常会产生不令人满意的结果，例如由于相机运动，动态内容的处理不当以及局部区域的不适当地融合（例如，曝光过度或曝光不足）而导致的模糊/重影伪影。此外，它们通常需要高质量的图像配准，这在深度变化较大且动态纹理较大的情况下很难实现，而且非常耗时。在本文中，我们建议通过单个单应性进行粗略配准，并无缝组合输入以隐藏任何可能的未对准。具体而言，该方法首先使用马尔可夫随机场（MRF）能量来标记所有像素，这会将不同的标记分配给不同的对齐输入图像。在贴标签过程中，它会选择暴露的区域，并同时跳过移动的对象。然后，该方法根据标签对拉普拉斯图像进行组合，并通过求解泊松方程构造融合结果。此外，在求解泊松方程以平衡和改善融合结果时，它增加了一些内部约束。我们提出了各种具有挑战性的示例，包括静态/动态，室内/室外以及白天/夜间场景，以证明所提出方法的有效性和实用性。在求解泊松方程以平衡和改善融合结果时，它增加了一些内部约束。我们提出了各种具有挑战性的示例，包括静态/动态，室内/室外以及白天/夜间场景，以证明所提出方法的有效性和实用性。在求解泊松方程以平衡和改善融合结果时，它增加了一些内部约束。我们提出了各种具有挑战性的示例，包括静态/动态，室内/室外以及白天/夜间场景，以证明所提出方法的有效性和实用性。