Image spatial diffusion on GPUs

Lihua Zhu, Charles Wang, Guangfei Zhu, Bo Han, Heng Wang, Pei Huang, Enhua Wu
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引用次数: 4

Abstract

Image spatial diffusion targets on blurring small discontinuities while sharpening distinct edges. We propose two GPU-based methods to efficient generate content-aware spatial diffused images. In the first method, we apply an enhanced bilateral filter on the input image. Our filter adopts a spatial weight kernel in the form of an inverted Gaussian. This inverted-Gaussian-spatial bilateral filter (IGSB) can remove small spots in large smooth areas more efficiently than Gaussian bilateral filter that are commonly used. In the second method, we present a fast approximation of mean-shift algorithm on GPUs. Considering the parallel nature of GPUs, the process of mean-shift kernel density estimation is modified and improved to achieve satisfied color image diffusion effects.
gpu上的图像空间扩散
图像空间扩散的目标是模糊小的不连续性,同时锐化明显的边缘。我们提出了两种基于gpu的方法来高效地生成内容感知的空间扩散图像。在第一种方法中,我们对输入图像应用增强的双边滤波器。我们的滤波器采用倒高斯形式的空间权核。这种反高斯空间双边滤波器(IGSB)可以比常用的高斯双边滤波器更有效地去除大面积光滑区域中的小斑点。在第二种方法中,我们在gpu上提出了一种快速逼近均值移位算法。考虑到gpu的并行特性,对mean-shift核密度估计过程进行了改进,以获得满意的彩色图像扩散效果。
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