A COMPARATIVE STUDY OF COMMON EDGE DETECTION OPERATORS IN DIGITAL IMAGE PROCESSING

Edge detection is a fundamental process in image processing that extracts information about the image and facilitates image segmentation and feature extraction. It has many applications in various fields of computer vision. Thus, it is very necessary to understand the performance of each of these edge detectors. This paper presents a comparative study of common edge detection operators in image processing using mean squared error (MSE), peak signal to noise ratio (PSNR), and Execution time (Et). The paper shows the canny edge detector is computationally expensive but provides higher accuracy in edge detection with higher PSNR and lower MSE. The software tool used in the project is MATLAB SIMULINK R2020a.