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Parallelization implementation of the multi-scale retinex image-enhancement algorithm based on amany integrated core platform

机译:基于AMANY集成核心平台的多尺度RETINEX图像增强算法的并行化实现

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Image-enhancement algorithms, for example, median filtering algorithms, Gaussian filtering algorithms, and the multiscale Retinex (MSR) algorithm, are widely used in unmanned aerial vehicle (UAV) image processing to resolve the problems of poor clarity, insufficient contrast, and weak adaptability of the aerial images. Aiming to improve the low efficiency of processing a large volume of UAV images using the MSR algorithm, this research realized a parallel MSR algorithm using the OpenMP programming model based on Intel's many integrated core (MIC) platform. First, the principle and serial implementation of the MSR algorithm were reviewed in detail, and the algorithm's hotspots were determined with the Intel VTune tool. Then, the corresponding parallel algorithm was designed and implemented. After checking the correctness of the parallel algorithm, systematical experiments on UAV images of different sizes were carried out. According to the experiments performed in the course of this work, the parallel MSR interpolation algorithm attained a speedup factor of 32 on two Intel MIC acceleration cards with 60 cores, which indicated that the parallel algorithm greatly reduced processing time and maximized speed and performance.
机译:图像增强算法,例如,中值滤波算法,高斯滤波算法和多尺度视网膜(MSR)算法,广泛应用于无人的空中车辆(UAV)图像处理,以解决清晰度差,对比度不足,对比度不足的问题空中图像的适应性。旨在提高使用MSR算法加工大量的UAV图像的低效率,本研究实现了使用基于英特尔许多集成核心(MIC)平台的OpenMP编程模型的并联MSR算法。首先,详细审查了MSR算法的原理和串行实现,并使用英特尔VTune工具确定了算法的热点。然后,设计并实现了相应的并行算法。在检查并行算法的正确性后,执行了不同尺寸的UAV图像的系统实验。根据在该工作过程中进行的实验,并行MSR插值算法在具有60个核心的两个英特尔MIC加速卡上实现了32的加速因子,这表明并行算法大大降低了处理时间和最大化的速度和性能。

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