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.

中文翻译：

---

基于多核集成平台的多尺度retinex图像增强算法的并行化实现

图像增强算法，例如中值滤波算法、高斯滤波算法和多尺度 Retinex（MSR）算法，被广泛应用于无人机（UAV）图像处理中，以解决清晰度差、对比度不足和弱的问题。航拍图像的适应性。针对MSR算法在处理大量无人机图像时效率低下的问题，本研究采用基于Intel多核（MIC）平台的OpenMP编程模型，实现了并行MSR算法。首先详细回顾了MSR算法的原理和串行实现，并利用Intel 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，对不同尺寸的无人机图像进行了系统实验。根据在这项工作过程中进行的实验，并行 MSR 插值算法在两块 60 核的 Intel MIC 加速卡上获得了 32 的加速因子，这表明并行算法大大减少了处理时间，并最大限度地提高了速度和性能。