GPU-Oriented Parallel Algorithm for Histogram Statistical Image Enhancement

Histogram statistics has important applications in the fields of image enhancement and target detection. However, with the increasing size of the image and the higher real-time requirements, the processing process of the histogram statistical local enhancement algorithm is slow and cannot reach the...

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Veröffentlicht in:Jisuanji kexue yu tansuo Jg. 16; H. 10; S. 2273 - 2285
1. Verfasser: XIAO Han, SUN Lupeng, LI Cailin, ZHOU Qinglei
Format: Journal Article
Sprache:Chinesisch
Veröffentlicht: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 01.10.2022
Schlagworte:
histogram statistics|local enhancement|local mean|graphics processing unit (gpu)|compute unified device architecture (cuda)|parallel algorithm
ISSN:1673-9418
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Abstract Histogram statistics has important applications in the fields of image enhancement and target detection. However, with the increasing size of the image and the higher real-time requirements, the processing process of the histogram statistical local enhancement algorithm is slow and cannot reach the expected satisfactory speed. In view of this deficiency, this paper realizes the parallel processing of histogram statistical image enhancement algorithm on graphics processing unit (GPU) platform, which improves the processing speed of large format digital images. Firstly, the efficiency of data access is improved by making full use of compute unified device architecture (CUDA) active thread block and active thread to process different sub-image blocks and pixels in parallel. Then, the paralle-lization of histogram statistical image enhancement algorithm on GPU platform is realized by using kernel configu-ration parameter optimization and data parallel computing technology. Finally, the efficient data transmission
AbstractList Histogram statistics has important applications in the fields of image enhancement and target detection. However, with the increasing size of the image and the higher real-time requirements, the processing process of the histogram statistical local enhancement algorithm is slow and cannot reach the expected satisfactory speed. In view of this deficiency, this paper realizes the parallel processing of histogram statistical image enhancement algorithm on graphics processing unit (GPU) platform, which improves the processing speed of large format digital images. Firstly, the efficiency of data access is improved by making full use of compute unified device architecture (CUDA) active thread block and active thread to process different sub-image blocks and pixels in parallel. Then, the paralle-lization of histogram statistical image enhancement algorithm on GPU platform is realized by using kernel configu-ration parameter optimization and data parallel computing technology. Finally, the efficient data transmission
Author XIAO Han, SUN Lupeng, LI Cailin, ZHOU Qinglei
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  fullname: XIAO Han, SUN Lupeng, LI Cailin, ZHOU Qinglei
  organization: 1. School of Information Science and Technology, Zhengzhou Normal University, Zhengzhou 450044, China;2. School of Civil and Architectural Engineering, Shandong University of Technology, Zibo, Shandong 255000, China;3. School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
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Snippet Histogram statistics has important applications in the fields of image enhancement and target detection. However, with the increasing size of the image and the...
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SubjectTerms histogram statistics|local enhancement|local mean|graphics processing unit (gpu)|compute unified device architecture (cuda)|parallel algorithm
Title GPU-Oriented Parallel Algorithm for Histogram Statistical Image Enhancement
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