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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >An improved hyper smoothing function based edge detection algorithm for noisy images
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An improved hyper smoothing function based edge detection algorithm for noisy images

机译：基于噪声图像的改进的超平滑功能的边缘检测算法

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摘要

This paper presents an improved hyper smoothing function based methodology for efficient edge detection. The main aim of this work is to obtain localized edges of noisy and blurred images without duplicate ones and integrating them into meaningful object boundaries. Therefore, logarithmic hyper-smoothing function is introduced in local binary pattern leading to improved hyperfunction based local binary pattern (IHLBP) algorithm. The proposed technique uses an improved counting scheme to correctly evaluate the number of image points having pixel value greater than or equal to the central pixel. The IHLBP algorithm is tested on synthetic images, radiography images, real-life pictures from USC-SIPL and BSDS database. Improved local binary pattern (ILBP), hyper local binary pattern (HLBP), Canny and Sobel methods are also used for comparative analysis. The results reveal that the proposed algorithm performs well on all synthetic and real images in the presence of blur and salt & pepper noise. Thus IHLBP proves to be an effective approach for edge detection in comparison to conventional methods.
机译：本文提出了一种改进的超平滑功能的有效边缘检测方法。这项工作的主要目的是获得噪声的局部边缘和模糊图像，无需重复，并将它们集成为有意义的对象边界。因此，在局部二进制模式中引入了对数超平滑功能，导致改进的基于超功能的局部二进制模式（IHLBP）算法。所提出的技术使用改进的计数方案来正确地评估具有大于或等于中心像素的像素值的图像点的数量。从USC-SIPL和BSDS数据库上测试IHLBP算法在合成图像，造影图像，现实寿命图片上进行测试。改进的局部二进制图案（ILBP），超局部二进制图案（HLBP），Canny和Sobel方法也用于比较分析。结果表明，该算法在存在模糊和盐和辣椒噪声存在下对所有合成和真实图像进行良好。因此，与传统方法相比，IHLBP证明是边缘检测的有效方法。

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