Abstract: Image quality is of crucial importance in modern digital technologies. This paper analyzes the computational challenges in image processing, with an emphasis on improving performance through parallel computing. The focus is on the implementation of efficient parallel models and software solutions using filtering techniques. In the framework of the study, a parallel model based on filters is created and tested through a multi-threaded parallel software implementation. This implementation applies a set of filters to a list of compressed images and generates output results for each filter, which allows for an analysis of their effectiveness. The filters Robert, Binary Threshold, Black and White, and UV are selected due to their broad applicability in various domains. Scalability analyses and performance evaluations show that the proposed parallel computing model is highly scalable and can be adapted to different hardware configurations. The article also presents an overview of alternative edge detection algorithms (Sobel, Canny, Prewitt), classical noise reduction approaches (Gaussian Blur, Median Filtering), a comparison between multi-threaded CPU implementations and GPU-based parallel computing, as well as an application of machine learning methods for automated image filtering and segmentation, which opens new possibilities for future research.

Keywords: CPU Acceleration, Edge Detection, Image Filtering, Machine Learning, Multithreading, Noise Reduction, Parallel Computing

Pages: 131-141