Abstract
As a classic and effective edge detection operator, the Sobel operator has been widely used in image segmentation and other image processing technologies. This operator has obvious advantages in the speed of extracting the edge of images, but it also has the disadvantage that the detection effect is not ideal when the image contains noise. In order to solve this problem, this paper proposes an optimized scheme for edge detection. In this scheme, the weighted nuclear norm minimization (WNNM) image denoising algorithm is combined with the Sobel edge detection algorithm, and the excellent denoising performance of the WNNM algorithm in a noise environment is utilized to improve the anti-noise performance of the Sobel operator. The experimental results show that the optimization algorithm can obtain better detection results when processing noisy images, and the advantages of the algorithm become more obvious with the increase of noise intensity.
Highlights
The Sobel edge detection algorithm based on weighted nuclear norm minimization (WNNM) denoising proposed in this paper, the traditional Sobel edge detection algorithm and the improved edge detection algorithm based on median filtering proposed in Reference [3] will be applied to the Cameraman image, respectively
The detection result of the traditional Sobel operator has been completely submerged in a large amount of noise, and the edge pixels cannot be distinguished
10–12 uescorresponding of mean square error (MSE), peak signal-tonoise ratio (PSNR), and of the tested algorithms, and Figures 10–12 are the correline graphs, sponding line graphs, respectively. In this experiment, compared with the other algorithms listed, the MSE of the proposed algorithm decreases by 0.0138–0.0633, the PSNR increases by 1.5558–4.2135 dB and the structural similarity index metric (SSIM) increases by 0.0663–0.3909
Summary
Image edge means the end of one area and the beginning of another area in an image. The collection of pixels at the junction of adjacent areas in the image constitutes the edge of the image [1]. Topno et al proposed an improved edge detection method based on median filtering. Yoon et al proposed an edge detection method based on the Bhattacharyya distance with adjustable block space. In this algorithm, in order to calculate the Bhattacharyya distance, a pair of blocks were extracted for each pixel. Chetia et al proposed a quantum-improved Sobel edge detection algorithm with nonmaximum suppression. In this algorithm, the Sobel operator with 45◦ and 135◦ direction masks was used for quantum on Weighted Nuclear Norm. The experimental results verified that the proposed algorithm has better anti-noise performance and can obtain clear and continuous edge information under high noise levels, confirming the effectiveness of the algorithm
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