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Multifractal detrended fluctuation analysis parallel optimization strategy based on openMP for image processing

  • Advances in Parallel and Distributed Computing for Neural Computing
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Abstract

In the past few years, multifractal detrended fluctuation analysis (MF-DFA) method has been widely applied in the field of agricultural image processing. However, the agricultural image feature MF-DFA analyses involves a great deal of iterative processes and complex matrix operations, which require massive computation and processing time. In order to reduce processing time and improve analysis efficiency, we first develop a MF-DFA program that involves image preprocessing, image segmentation, local area accumulation matrix calculation, local area trend fitting, local area trend elimination, a global qth-order fluctuation function, and the Hurst index. Then, we analyze and compare MF-DFA each modules’ performance characteristics and explore its parallelism according to various segmentation scales s. Lastly, we propose a parallel optimization scheme based on OpenMP for the MF-DFA. The results of our rigorous performance evaluation clearly demonstrate that our proposed parallel optimization scheme can efficiently use multicore capability to extract rape leaf image texture characteristics.

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Acknowledgements

This research was partially funded by the National Key Research and Development Program of China(Grant No. 2018YFB0204004), Hunan Provincial Key Research and Development Program (Grant No. 2018GK2055), National Natural Science Foundation of China (Grant No. 61370098, 61672219), Double first-class construction project of Hunan Agricultural University (Grant No. SYL201802029).

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Authors and Affiliations

  1. College of Information Science and Technology/Southern Regional Collaborative Innovation Center for Grain and Oil Crops in China, Hunan Agricultural University, Changsha, 410128, China

    Xiaoyong Tang & Xiaopan Yang

  2. School of Information Science and Engineering, Hunan University, Changsha, 410082, China

    Xiaoyong Tang & Fan Wu

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  1. Xiaoyong Tang
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  2. Xiaopan Yang
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  3. Fan Wu
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Correspondence to Xiaoyong Tang.

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Tang, X., Yang, X. & Wu, F. Multifractal detrended fluctuation analysis parallel optimization strategy based on openMP for image processing. Neural Comput & Applic 32, 5599–5608 (2020). https://doi.org/10.1007/s00521-019-04164-2

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  • DOI: https://doi.org/10.1007/s00521-019-04164-2

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