Fuzzy Edge Detection Using Wavelet and Adaptive Median Filter for Corrupted Image By Salt & Pepper Noise

ABSTRACT: The goal of edge detection in image processing is to determine the frontiers of all represented objects, based on automatic processing of color or gray level information contained in each pixel. This procedure has many applications in image processing, computer vision and biological and robotic vision [۱], [۲], and [۳]. Edge detection is an important preprocessing step in image analysis. Successful results of image analysis extremely depend on edge detection [۱]. This paper presents a new approach for edge detection in situations where the image is corrupted by noise. Traditional edge detections are sensitive to noise. The structure of our proposed edge detector, to make the process robust against noise, is a combination of wavelet transform, fuzzy inference system and adaptive median filter. The proposed method is tested under noisy conditions on several images and also compared with conventional edge detectors such as Sobel and Prewitt and Canny . Experimental results reveal that the proposed method exhibits better performance and may efficiently be used for the detection of edges in images corrupted by noise.