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首页> 外文期刊>Kybernetes: The International Journal of Systems & Cybernetics >Edge detection for highly distorted images suffering Gaussian noise based on improve Canny algorithm
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Edge detection for highly distorted images suffering Gaussian noise based on improve Canny algorithm

机译：基于改进Canny算法的高斯噪声高失真图像边缘检测

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摘要

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.
机译：目的-本文的目的是检测受高斯噪声影响的高噪声水平的图像边缘。设计/方法/方法-当Canny边缘检测算法应用于受高斯噪声影响的高度失真的图像时，效果较差。在Canny算法中，二维高斯函数用于去除噪声并保留边缘。在高噪声水平下，二维高斯功能无法满足需求。本文提出了一种改进的Canny边缘检测算法。提出的算法基于局部线性核平滑，其中局部邻域适合于由观测数据测量的表面的局部平滑度。因此，该程序可以正确消除表面的连续性区域中的噪声，并同时保留不连续性。研究结果-去除噪声和保留边缘的统计模型可以满足在高斯噪声严重破坏的图像中进行边缘检测的需求。研究的局限性/意义-发现当噪声比高于40％时，边缘检测算法的性能较差。实际意义-一种非常有用的方法，用于检测遭受高斯噪声影响的高度失真的图像。独创性/值-由于可以将图像视为图像强度函数的一个表面，并且该表面在对象轮廓处具有不连续性，因此该算法可以直接应用于检测高噪声级别的图像边缘。

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