A comparative study of edge detectors in digital image processing

Edge detection is one of the most fundamental operations in image processing and is one of the most commonly used operations in image processing and pattern recognition. The reason for this is that edges form the outline of an object and thus reduce the size of file without losing the useful information. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. Edge detection reduces the amount of data needed to process by removing unnecessary features. Knowing the positions of these boundaries is critical in the process of image enhancement, recognition, restoration and compression. The edges of image are considered to be most important image attributes that provide valuable information for human image perception. The areas of this work are in digital image process and telecommunication engineering, which are very wide fields. In this paper a comparison of different edge detectors has been made and results formed using the values of mean square error and peak signal to noise ratio shows that intuitionistic fuzzy edge detector outperform over the existed edge detectors.