A General Framework for Bilateral and Mean Shift Filtering
A General Framework for Bilateral and Mean Shift Filtering
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发表时间：
2014-04
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通讯作者：
J. Solomon;Keenan Crane;Adrian Butscher;C. Wojtan
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作者：
J. Solomon;Keenan Crane;Adrian Butscher;C. Wojtan
我们提出了一个广义的双边滤波器，可应用于图像，网格和其他领域内的一个统一的框架内的信号的特征保持平滑。我们的离散化在精度和速度方面与最先进的平滑技术具有竞争力，易于实现，并且具有易于理解的参数。与以前的双边过滤器开发的网格和其他不规则的领域，我们的建设减少了完全的图像双边矩形域，并配备了严格的基础，在光滑和离散的设置。这些保证使我们能够构建无条件收敛的均值漂移计划，处理各种极其嘈杂的信号。我们还将我们的框架应用于几何边缘保持效果，如特征增强，并展示了它与局部直方图技术的关系。
We present a generalization of the bilateral filter that can be applied to feature-preserving smoothing of signals on images, meshes, and other domains within a single unified framework. Our discretization is competitive with state-of-the-art smoothing techniques in terms of both accuracy and speed, is easy to implement, and has parameters that are straightforward to understand. Unlike previous bilateral filters developed for meshes and other irregular domains, our construction reduces exactly to the image bilateral on rectangular domains and comes with a rigorous foundation in both the smooth and discrete settings. These guarantees allow us to construct unconditionally convergent mean-shift schemes that handle a variety of extremely noisy signals. We also apply our framework to geometric edge-preserving effects like feature enhancement and show how it is related to local histogram techniques.