Image Completion Using Global Optimization
Image Completion Using Global Optimization
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DOI：
10.1109/cvpr.2006.141
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发表时间：
2006-06
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影响因子：
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通讯作者：
N. Komodakis
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文献类型：
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作者：
N. Komodakis
本文提出了一种基于样本的图像修补框架，将图像修补、纹理合成和图像修复有机地结合在一起。与现有的贪婪技术相反，这些任务以具有明确定义的目标函数的离散全局优化问题的形式提出。为了解决这个问题，提出了一种新的优化方案，称为优先级BP，它进行了两个非常重要的扩展标准的信念传播（BP）：“基于优先级的消息调度”和“动态标签修剪”。这两个扩展协同工作，以处理由大量现有标签引起的BP难以忍受的计算成本。此外，这两种扩展都是通用的，因此也可以应用于任何MRF能量函数。我们的方法的有效性证明了各种各样的图像完成的例子。
A new exemplar-based framework unifying image completion, texture synthesis and image inpainting is presented in this work. Contrary to existing greedy techniques, these tasks are posed in the form of a discrete global optimization problem with a well defined objective function. For solving this problem a novel optimization scheme, called Priority- BP, is proposed which carries two very important extensions over standard belief propagation (BP): "prioritybased message scheduling" and "dynamic label pruning". These two extensions work in cooperation to deal with the intolerable computational cost of BP caused by the huge number of existing labels. Moreover, both extensions are generic and can therefore be applied to any MRF energy function as well. The effectiveness of our method is demonstrated on a wide variety of image completion examples.