Total variation minimization and a class of binary MRF models
Total variation minimization and a class of binary MRF models
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DOI：
10.1007/11585978_10
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发表时间：
2005-01-01
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通讯作者：
Chambolle, A
中科院分区：
文献类型：
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作者：
Chambolle, A
我们观察到，一类简单的二进制MRF能量与鲁丁 - X骨 - 佛特米（ROF）的总变异方法之间的整个类别的二元MRF能量之间存在很强的联系。我们更准确地说，可以通过最大程度地减少适当的ROF问题，反之亦然来找到二进制MRF的解决方案。这导致了新的算法。然后，我们比较各种算法的效率。
We observe that there is a strong connection between a whole class of simple binary MRF energies and the Rudin-Osher-Fatemi (ROF) Total Variation minimization approach to image denoising. We show, more precisely, that solutions to binary MRFs can be found by minimizing an appropriate ROF problem, and vice-versa. This leads to new algorithms. We then compare the efficiency of various algorithms.