For hyperspectral imaging, the vector bilateral filter usually leads to better performance when compared with the traditional 2D bilateral filter. However, the large computation complexity of vector bilateral filtering makes it an extremely time cost algorithm. To overcome this challenge, a GPU‐based acceleration for vector bilateral filtering called vBF_GPU was proposed in this paper. To improve the efficiency of the cache memory usage, multiple CUDA threads were utilized to processing one pixel of the hyperspectral image in vBF_GPU. The memory access operation of vBF_GPU was fully optimized to reduce the memory access cost of the GPU program. The experiment results indicated that vBF_GPU can provide more than 30× speedup when compared with an octa‐core CPU implementation and more than 20× speedup when compared with a naïve GPU implementation of vector bilateral filtering.

中文翻译：

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使用GPU进行矢量双边滤波加速高光谱成像

对于高光谱成像，与传统的2D双边滤波器相比，矢量双边滤波器通常会带来更好的性能。然而，矢量双边滤波的巨大计算复杂度使其成为一种极其耗时的算法。为克服这一挑战，本文提出了一种基于GPU的矢量双向过滤加速，称为vBF_GPU。为了提高缓存使用的效率，利用多个CUDA线程在vBF_GPU中处理高光谱图像的一个像素。vBF_GPU的内存访问操作已完全优化，以降低GPU程序的内存访问成本。实验结果表明，vBF_GPU能提供超过30 ×当用八芯CPU实现超过20比较，并加速× 与单纯的矢量双边滤波GPU实现相比，速度更快。