Edge detection for highly distorted images suffering Gaussian noise based on improve Canny algorithm
Purpose - The purpose of this paper is to detect edge of image in high noise level, suffering Gaussian noise. Design/methodology/approach - Canny edge detection algorithm performs poorly when applied to highly distorted images suffering from Gaussian noise. In Canny algorithm, 2D-gaussian function is used to remove noise and preserve edge. In high noise level, 2D-gaussian function cannot meet the needs. In this paper, an improving Canny edge detection algorithm is presented. The algorithm presented is based on local linear kernel smoothing, in which local neighborhoods are adapted to the local smoothness of the surface measured by the observed data. The procedure can therefore remove noise correctly in continuity regions of the surface, and preserve discontinuities at the same time. Findings - The statistical model of removing noise and preserving edge can meet the need of edge detection in images highly corrupted by Gaussian noise. Research limitations/implications - It was found that when the noise ratio is higher than 40 percent, the edge detection algorithm performs poorly. Practical implications - A very useful method for detecting highly distorted images suffering Gaussian noise. Originality/value - Since an image can be regarded as a surface of the image intensity function and such a surface has discontinuities at the outlines of objects, this algorithm can be applied directly to detect edge of image in high noise level.
cybernetics gaussian processes image processing