Akademik Veri Yönetim Sistemi
Signal, Image and Video Processing, 2024 (SCI-Expanded)
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.