Unsharp masking image enhancement the parallel algorithm based on cross-platform

In view of the low computational efficiency and the limitations of the platform of the unsharp masking image enhancement algorithm, an unsharp masking image enhancement parallel algorithm based on Open Computing Language (OpenCL) is proposed. Based on the analysis of the parallel characteristics of...

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Bibliographic Details
Published in:Scientific reports Vol. 12; no. 1; pp. 20175 - 17
Main Authors: Song, Yupu, Li, Cailin, Xiao, Shiyang, Xiao, Han, Guo, Baoyun
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 23.11.2022
Nature Publishing Group
Nature Portfolio
Subjects:
639/705/117
639/705/794
Algorithms
Computer applications
Data storage
Humanities and Social Sciences
Image Enhancement
Language
multidisciplinary
Names
Science
Science (multidisciplinary)
Time Perception
Transplantation
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:In view of the low computational efficiency and the limitations of the platform of the unsharp masking image enhancement algorithm, an unsharp masking image enhancement parallel algorithm based on Open Computing Language (OpenCL) is proposed. Based on the analysis of the parallel characteristics of the algorithm, the problem of unsharp masking processing is implemented in parallel. Making use of the characteristics of data reuse in the algorithm, the effective allocation and optimization of global memory and constant memory are realized according to the access attributes of the data and the characteristics of the OpenCL storage model, and the use efficiency of off-chip memory is improved. Through the data storage access mode, the fast computing local memory access mode is discovered, and the logical data space transformation is used to convert the storage access mode, so as to improve the bandwidth utilization of the on-chip memory. The experimental results show that, compared with the CPU serial algorithm, the OpenCL accelerated unsharp masking image enhancement parallel algorithm greatly reduces the execution time of the algorithm while maintaining the same image quality, and achieves a maximum speedup of 16.71 times. The high performance and platform transplantation of the algorithm on different hardware platforms are realized. It provides a reference method for real-time processing of a large amount of data of high-resolution images for image enhancement.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-21745-9