Clustering-based Acceleration for High-dimensional Gaussian Filtering
Clustering-based Acceleration for High-dimensional Gaussian Filtering
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DOI：
10.5220/0010548600650072
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Sou Oishi;Norishige Fukushima
中科院分区：
文献类型：
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作者：
Sou Oishi;Norishige Fukushima
：边缘保持滤波是图像处理应用程序的重要工具，具有各种类型的滤波。对于实时应用，加速其速度也是必不可少的。为了加速各种类型的边缘保持滤波，我们通过高维高斯滤波来表示各种边缘保持滤波。然后，我们加速高维高斯滤波基于聚类的常数算法，它具有O（K）阶，其中K是簇的数量。基于聚类的方法是为彩色双边滤波而开发的;然而，本文将其用于高维双边滤波。此外，配合平铺、k-means++和主成分分析，我们可以进一步提高滤波器的性能。实验结果表明，该方法可以通过近似聚类的高维高斯滤波来近似各种边缘保持滤波.
: Edge-preserving filtering is an essential tool for image processing applications and has various types of fil-tering. For real-time applications, acceleration of its speed is also essential. To accelerate various types of edge-preserving filtering, we represent various edge-preserving filtering by high-dimensional Gaussian fil-tering. Then, we accelerate the high-dimensional Gaussian filtering by clustering-based constant algorithm, which has O ( K ) order, where K is the number of clusters. The clustering-based method was developed for color bilateral filtering; however, this paper used it for high-dimensional bilateral filtering. Also, cooperating with tiling, k-means++, and principal component analysis, we can further improve the filter’s performance. Experimental results show that our method can approximate various edge-preserving filtering by approximated clustering-based high-dimensional Gaussian filtering.