Title

Acceleration of Vector Bilateral Filtering for Hyperspectral Imaging With GPU

Document Type

Article

Publication Date

2-18-2021

Publication Title

International Journal of Circuit Theory and Applications

First page number:

1

Last page number:

13

Abstract

© 2021 John Wiley & Sons, Ltd. 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.

Keywords

3D-convolution; GPU; Memory access optimization; Vector bilateral filtering

Disciplines

Computer Sciences

Language

English

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