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Multiscale edge detection of noisy images using wavelets

机译:使用小波的多尺度噪声图像边缘检测

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The classical edge detectors work fine with the high quality pictures, but often are not good enough for noisy images because they cannot distinguish edges of different significance. The paper presented a novel approach to multiscale edge detection for noisy images using wavelet transforms based on Lipschitz regularity coefficients and a cascade algorithm. The relationship between wavelet transform and Lipschitz regularity was established. The proposed wavelet based edge detection algorithm combined the coefficients of wavelet transforms a-long with a cascade algorithm which significantly improves the result. The comparison between the proposed method and the classical edge detectors was carried out. The algorithm was applied to various images and its performance was discussed. The results of edge detection of contaminated images using the proposed algorithm show that it works better than the classical edge detectors.
机译:经典的边缘检测器可以很好地处理高质量图片,但是对于嘈杂的图像通常不够好,因为它们无法区分不同重要性的边缘。本文提出了一种基于Lipschitz正则系数和级联算法的小波变换多尺度边缘检测噪声图像的方法。建立了小波变换与Lipschitz正则关系。提出的基于小波的边缘检测算法将小波变换的a-long系数与级联算法相结合,大大提高了结果。将该方法与经典边缘检测器进行了比较。该算法被应用于各种图像,并讨论了其性能。使用该算法对污染图像进行边缘检测的结果表明,该算法比传统的边缘检测器效果更好。

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