Digital Image Edge Detection Techniques Comparative Analysis

Abstract

In digital image processing, edge detection is a process for getting the boundaries of objects in digital image, structural image and removing the unwanted area from digital image. Edge is very important part of any image, having basic information, which can be manipulated by different edge detection techniques. The edge detection is a digital image processing technique to find the boundaries or edges of an image or object through the brightness discontinuity. There are many operators to get boundaries or edges but we need more effective and accurate methods. In this research work, edge detections methodologies and their mathematical formulas are investigated and detailed concepts of fundamental operations of edge detection techniques and their comparison are presented. This research work will provide a comparison of hybrid technique that combine second order derivative technique Log and Canny, With Conventional Sobel, Prewitt, Roberts, Canny and Log Operators of edge detection, with regard to visual inspection, and image quality parameters like mean square error (MSE), Root mean square error (RMSE), Mean Absolute Error (MAE), Peak signal to noise ratio (PSNR), signal to noise ratio (SNR), Bit error, Structural similarity index (S.S.I.M)., Average Difference (AD), Structural Content (S.C.), Normalized Cross Correlation (NCC), Normalized Absolute Error (NAE). The comparative analysis of Edge Detectors taken against commonly encountered noise like Salt and Pepper noise, Gaussian Noise, Poisson Noise, Speckle Noise. Simulation of edge detection techniques are carried out in Matlab and the comparison is made on the basis of the Matlab results and the values of different image quality parameters. newline