Resilient EDGE Detection Method for Significant IMPULSE Noise

Abstract

The present study introduces a novel technique for detecting edges in images by means of the Switching Adaptive Median and Fixed Weighted Mean (SAMFWM) filter, which proves to be highly effective in removing impulse noise compared to conventional denoising filters that are currently available, while preserving edge details, thus ensuring optimal edge detection. The performance of the proposed approach is assessed using a comprehensive analysis of different performance metrics, including Mean Square Error (MSE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR). In addition, the Sobel operator is used to detect the edges and Non-Maximum Suppression is used to track and thin the edges. These techniques are utilized to handle edge discontinuities and detect edges in the presence of high-intensity noise. Furthermore, the proposed approach outperforms other alternative techniques such as the Robert, Prewitt, and Canny edge detectors in effectively removing impulse noise, even at high levels.