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Infrared Image Edge Extraction of Cable Terminal Based on Improved Eight Direction Sobel Operator

机译:基于改进的八向Sobel算子的电缆终端红外图像边缘提取

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The infrared thermal image is an importance in condition monitoring of electrical equipment. However, the infrared image edge is coarse, fuzzy, and with serious noise and brings great inconvenience to the infrared image processing. An adaptive optimal threshold edge extraction algorithm based on improved Sobel operator is proposed in this paper. In the algorithm, by eight-direction Sobel operators the infrared image edge is extracted in the high temperature area in noisy environment. At the same time, the wavelet coefficients in sub-band are described by general Gaussian distribution and the variance is estimated from the local neighborhood information of sub-band wavelet coefficients so that the adaptive optimal denoising threshold can be obtained. Finally, the edge extraction infrared image is denoised by combining with improved Sobel operator and the optimal threshold. Matlab simulation results show that the algorithm can effectively detect the edge of infrared image and greatly improve its anti-noise ability.
机译:红外热图像在电气设备状态监测中很重要。然而,红外图像边缘粗糙,模糊,噪声严重,给红外图像处理带来很大的不便。提出了一种基于改进的Sobel算子的自适应最优阈值边缘提取算法。在该算法中,由八向Sobel算子在高温环境中的高温区域提取红外图像边缘。同时,利用一般的高斯分布描述子带中的小波系数,并根据子带小波系数的局部邻域信息估计方差,从而获得自适应的最佳去噪阈值。最后,结合改进的Sobel算子和最优阈值对边缘提取红外图像进行去噪。 Matlab仿真结果表明,该算法可以有效地检测出红外图像的边缘,大大提高了其抗噪能力。

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