Enhanced GPU-Based Anti-Noise Hybrid Edge Detection Method

Abstract

Today, there is a growing demand for computer vision and image processing in different areas and applications such as military surveillance, and biological and medical imaging. Edge detection is a vital image processing technique used as a pre-processing step in many computer vision algorithms. However, the presence of noise makes the edge detection task more challenging; therefore, an image restoration technique is needed to tackle this obstacle by presenting an adaptive solution. As the complexity of processing is rising due to recent high-definition technologies, the expanse of data attained by the image is increasing dramatically. Thus, increased processing power is needed to speed up the completion of certain tasks. In this paper,we present a parallel implementation of hybrid algorithm-comprised edge detection and image restoration along with other processes using Computed Unified Device Architecture (CUDA) platform, exploiting a Single Instruction Multiple Thread (SIMT) execution model on a Graphical Processing Unit (GPU). The performance of the proposed method is tested and evaluated using well-known images from various applications. We evaluated the computation time in both parallel implementation on the GPU, and sequential execution in the Central Processing Unit (CPU) natively and using Hyper-Threading (HT) implementations. The gained speedup for the naïve approach of the proposed edge detection using GPU under global memory direct access is up to 37 times faster, while the speedup of the native CPU implementation when using shared memory approach is up to 25 times and 1.5 times over HT implementation.