基于改进高斯-拉普拉斯算子的噪声图像边缘检测方法

针对现有梯度算子在图像边缘检测中存在的对噪声比较敏感的问题,提出了一种基于改进高斯-拉普拉斯算子的图像边缘检测方法。噪声图像中的边缘检测是一项关键任务,然而目前常用的几种梯度算子,包括已经提出的高斯-拉普拉斯算子都没能取得理想效果。提出的方法对传统的拉普拉斯边缘检测算子做了改进,并与高斯滤波器相结合。首先,应用高斯滤波器来平滑图像,抑制噪声。然后基于拉普拉斯梯度边缘检测器进行边缘检测。最后在标准图像上进行评估,评估结果显示,提出的边缘检测方法所获得的峰值信噪比(PSNR)和均方误差(MSE)均优于其他几种对比方法。

Noise Image Edge Detection Based on Improved Gauss - Laplace Operator

Aiming at the problem that the existing gradient operator is sensitive to noise in image edge detection, an image edge detection method based on improved Gauss - Laplace operator is proposed. Edge detection in noisy images is a key task, however, several commonly used gradient operators, including the proposed Gauss-Laplace operators, failed to achieve the desired results. The proposed method improves the traditional Laplace edge detection operator and combines with the Gauss filter. First, apply a Gauss filter to smooth the image and remove the noise. Edge detection is then based on a Laplace gradient edge detector. Finally, the standard images were evaluated. The evaluation results show that the proposed edge detection methods are superior to the comparative method.