Abstract

Image enhancement is frequently used to improve the input image’s visual quality. Experts also utilize image enhancement as a preprocessing method rather than a complete solution in computer vision applications. In addition, consumers want to acquire digital images with good real-life contrast, which balances the number of pixels with darker and brighter intensity values. Unfortunately, acquired images become too dark or bright to inspect visually due to bad lighting or unwanted reflections. These undesired images may cause problems in applications such as medical imaging, satellite imagery, or UAV imaging. Therefore, this study introduces an image enhancement method using local and global enhancements to overcome the above-mentioned issues. Besides image quality, there is another problem with image processing applications, such as image size. As the image size gets larger, computers usually take much more time to complete the given task. Parallel computing is a method that takes advantage of several processing units on the same system using some libraries or APIs. Since it is easy to use and requires fewer sequential code changes, we preferred to use OpenMP in this study to parallelize sequential implementation. Although the proposed work is developed in C++ and is based on a small sample of dark images, the findings suggest that proposed parallel implementations can be very efficient and feasible in other programming languages for different types of image processing operations. Then, we compared the performance of both sequential and parallel implementations. Based on the experimental results, we observed that the proposed parallel implementation of the reference algorithm runs up to 38 times faster than the sequential version on a cloud computing platform with 48 physical cores.

中文翻译：

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基于OpenMP的暗图像图像增强技术的并行实现

摘要

图像增强经常用于提高输入图像的视觉质量。专家们还利用图像增强作为一种预处理方法，而不是计算机视觉应用中的完整解决方案。此外，消费者希望获得具有良好现实对比度的数字图像，从而平衡像素数量与更暗和更亮的强度值。不幸的是，由于照明不良或不需要的反射，所获取的图像变得太暗或太亮而无法进行目视检查。这些不需要的图像可能会在医学成像、卫星图像或无人机成像等应用中引起问题。因此，本研究引入了一种使用局部和全局增强的图像增强方法来克服上述问题。除了图像质量之外，图像处理应用程序还存在另一个问题，例如图像大小。随着图像尺寸变大，计算机通常需要更多时间来完成给定任务。并行计算是一种使用某些库或 API 来利用同一系统上多个处理单元的方法。由于它易于使用并且需要较少的顺序代码更改，因此我们在本研究中首选使用 OpenMP 来并行化顺序实现。尽管所提出的工作是用 C++ 开发的，并且基于一小部分暗图像样本，但研究结果表明，对于不同类型的图像处理操作，所提出的并行实现在其他编程语言中可以非常有效且可行。然后，我们比较了顺序实现和并行实现的性能。根据实验结果，我们观察到，在具有 48 个物理核心的云计算平台上，所提出的参考算法的并行实现的运行速度比顺序版本快 38 倍。