Layer-based image completion by poisson surface reconstruction

Hengjin Liu, Huizhu Jia, Xiaodong Xie, Xiangyu Kong, Yuanchao Bai, Wen Gao
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引用次数: 0

Abstract

Image completion has been widely used to repair damaged regions of a given digital image in a visually plausible way. However, it is difficult to infer appropriate information, meanwhile keep globally coherent just from the origin image when its critical parts are missing. To address this problem, we propose a novel layer-divided image completion scheme, which contains two major steps. First, we extract foregrounds of both target image and source image, and then we apply a guided Poisson surface reconstruction technique to complete the target foreground according to parameters obtained from optimal-matching calculation. Second, to fill the remaining damaged part, a related exemplar-based image completion algorithm is further devised. Several experiments and comparisons show the effectiveness and robustness of our proposed algorithm.
基于图层的泊松曲面重建图像补全
图像补全已广泛用于修复受损区域的一个给定的数字图像在视觉上似是而非的方式。然而,当关键部分缺失时,很难推断出合适的信息,同时保持与原始图像的全局连贯。为了解决这个问题,我们提出了一种新的分层图像补全方案,该方案包含两个主要步骤。首先提取目标图像和源图像的前景,然后根据最优匹配计算得到的参数,应用一种引导泊松曲面重建技术完成目标前景。其次,设计了一种相关的基于样本的图像补全算法来填充剩余的损坏部分。实验和比较表明了该算法的有效性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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