Dongwei Wei
ABSTRACT
Since the traditional Canny edge detection algorithm has the problem of being susceptible to noise interference, which makes the algorithm unable to accurately extract the edge information of an image in a noisy environment, in order to solve this problem, this paper proposes a Canny edge detection algorithm based on sparse representation denoising. In this paper, the sparse representation denoising method based on K-SVD replaces the Gaussian filtering in the traditional Canny operator, which can ensure that the edge information of the image is well preserved while removing the noise ; the gradient templates of horizontal, vertical, 45°and 135°directions in Sobel operator are used to calculate the image gradient value, which not only reduces the missed detection rate of edges, but also improves the anti-jamming performance of the algorithm; Otsu is used to overcome the issue of artificially set double thresholds and improve the adaptiveness and accuracy of edge detection of traditional algorithms. From the experimental results, it is found that the proposed algorithm can extract edge information from images with Gaussian noise more accurately and adaptively, which has obvious advantages compared with the traditional Canny algorithm.
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Index Terms
- Canny Edge Detection Algorithm Based on Sparse Representation Denoising
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