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A multi-directional Gradient with Bi-Geometric Calculus to Detect Contours in Images with Multiplicative Noise

机译:具有双几何微积分的多向梯度,以检测具有乘法噪声的图像中的轮廓

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In this paper a new operator is presented for the detection of contours in images with multiplicative noise, by using the op-erations introduced in the bi-geometric calculus, since recent results in the literature show that multiplicative operators tend to make more accurate approximations of the reality in images with multiplicative noise. The operator introduced corresponds to a multiplicative multi-directional gradient. The Global Efficiency was used as performance function to make a comparison about the effectiveness in the detection of contours, between the multi-gradient and its multiplicative version. These operators are applied on some images (one synthetic and another real), under a threshold for each noise level, then the function of optimal performance is obtained over a continuous range of noise, and thus an objective comparison between both operators is presented. According to the results obtained from the objective comparison, the multiplicative multi-directional gradient operator presents improved efficiency in obtaining contours versus its classical version.
机译:在本文中,提供了一种新的操作员,用于检测具有乘法噪声的图像中的图像中的轮廓,通过使用在双面几何微积分中引入的运算术,因为文献中的最近结果表明,乘法操作者倾向于制作更准确的近似具有乘法噪声的图像的现实。介绍的操作员对应于乘法多向梯度。全局效率用作性能函数,以比较多梯度及其乘法版本之间检测轮廓的有效性。这些运营商应用于某些图像(一个合成和另一个实际),在每个噪声水平的阈值下,然后在连续的噪声范围内获得最佳性能的功能,因此呈现了两个运算符之间的客观比较。根据目标比较获得的结果,乘法多向梯度算子在获得轮廓与其经典版本中提高了提高的效率。

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