Parallel Implementation of Gaussian Filter Image Processing on a Cluster of Single Board Computer

Abstract

Gaussian filters are widely used in image processing applications, such as edge detection, segmentation, and feature extraction. However, computationally intensive computations can take a long time to process large images. Therefore, a parallel algorithm implementation is necessary to accelerate the process. The authors proposed the use of Orange Pi SBCs for parallel image processing tasks involving a Gaussian filter. This paper outlines the steps for implementing a parallel Gaussian filter on a cluster of SBCs. The performance of the parallel implementation was evaluated in terms of speedup and efficiency, which are essential parameters for measuring the effectiveness of the approach. The parallel implementation speedup is described as the ratio of the time required by the serial implementation to that required by the parallel implementation. The parallel implementation efficiency is described as the speedup ratio of the number of SBCs in a cluster. The results of the performance evaluation show that the parallel implementation of the Gaussian filter on a cluster of Orange Pi SBCs can achieve significant speedup and efficiency compared to the serial implementation. The speedup increases with the number of SBCs used in the cluster. Using four SBCs can result in a speedup of up to 2.1 times faster than serial implementation. The efficiency also increases with the number of SBCs used in the cluster. Using four SBCs could achieve an efficiency of up to 53.4%.