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Acceleration of vector bilateral filtering for hyperspectral imaging with GPU
Author(s) -
Chen Chong
Publication year - 2021
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.2973
Subject(s) - speedup , computer science , cuda , hyperspectral imaging , acceleration , filter (signal processing) , bilateral filter , computation , parallel computing , cache , pixel , artificial intelligence , computational science , algorithm , computer vision , physics , classical mechanics
Summary 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.