A Robust Edge Detector In The Presence Of Impulse Noise

Edge detection is a challenging task with many applications in image processing. Edge detection in images can be accomplished using various methods and well-known operators. The effectiveness of these techniques is frequently influenced by the extent of image detail preserved and the application at hand. Edge detection amplifies the noise, which has a major impact on their structural features. Impulse noise, often known as salt-and-pepper noise, is one such type of noise that affects the quality of images. Smoothing filters are frequently used to eliminate this kind of noise from images in order to reduce the noise's variation and maintain the image quality. The study presents a novel technique for detecting edges in images that utilizes denoising methods to retain image details while preventing blurring and preserving sharp edges along boundaries, even when high impulse noise is present. The effectiveness of the proposed approach is evaluated by measuring its performance using Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR), and is compared to the commonly used CANNY Edge detector.