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Edge-Constrained Image Compositing

机译:边缘约束图像合成

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The classic task of image compositing is complicated by the fact that the source and target images need to be carefully aligned and adjusted. Otherwise, it is not possible to achieve convincing results. Visual artifacts are caused by image intensity mismatch, image dis tortion or structure misalignment even if the images have been glob ally aligned. In this paper we extend classic Poisson blending by a constrained structure deformation and propagation method. This approach can solve the above-mentioned problems and proves use ful for a variety of applications, e.g. in de-ghosting of mosaic im ages, classic image compositing or other applications such as super resolution from image databases. Our method is based on the fol lowing basic steps. First, an optimal partitioning boundary is com puted between the input images. Then, features along this boundary are robustly aligned and deformation vectors are computed. Start ing at these features, salient edges are traced and aligned, serving as additional constraints for the smooth deformation field, which is propagated robustly and smoothly into the interior of the target im age. If very different images are to be stitched, we propose to base the deformation constraints on the curvature of the salient edges for C1-continuity of the structures between the images.
机译:图像合成的经典任务由于需要仔细对齐和调整源图像和目标图像而变得很复杂。否则,就不可能获得令人信服的结果。视觉伪像是由图像强度不匹配,图像失真或结构未对准引起的,即使图像已经全局对齐。在本文中,我们通过约束结构变形和传播方法扩展了经典的泊松混合。这种方法可以解决上述问题,并证明对于多种应用是有用的。消除马赛克图像,经典图像合成或其他应用程序(例如图像数据库的超分辨率)的重影。我们的方法基于以下基本步骤。首先,在输入图像之间计算最佳分割边界。然后,沿着该边界的特征进行稳健对齐并计算变形矢量。从这些特征开始,将跟踪并对齐显着边缘,这是对平滑变形场的附加约束,该变形场被鲁棒且平滑地传播到目标图像的内部。如果要拼接非常不同的图像,我们建议将变形约束基于显着边缘的曲率,以实现图像之间结构的C1连续性。

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