Digital image edge detection using optimized fuzzy clustering

Edges are one of the most important parts of an image. The border between two areas of the image that has a significant difference in brightness, color or texture is called an edge. Edge detection is a basic step in image segmentation and one of the most important steps to detect the characteristics of objects in an image. Recently, many researches have been carried out in the field of edge detection, each of which has tried to better detect the edges of the image. In this research, fuzzy theory and the concept of fuzzy clustering are used to detect the edge of digital images. At first, the pixels of the image are based on the amount of gray level. They are divided into clusters and then by determining the appropriate filters, the edge of the image is detected. The proposed method has recognized the edges of the image well, and has also shown remarkable performance in identifying the edges of the image that cannot be correctly recognized due to the inappropriate light level or noise. The results of this research showed that the proposed method has provided better results than usual methods in most cases and this is due to its good performance in detecting image details such as thickness and strong and weak edges, especially in images with lower resolution or lower contrast. It has performed very well.