Abstract
Multilevel image edge repair results directly affect the follow-up image quality evaluation and recognition. Current edge detection algorithms have the problem of unclear edge detection. In order to detect more accurate edge contour information, a multilevel image edge detection algorithm based on visual perception is proposed. Firstly, the digital image is processed by double filtering and fuzzy threshold segmentation; Through the analysis of the contour features of the moving image, the threshold of the moving image features is set, and the latest membership function is obtained to complete the multithreshold optimization. Adaptive smoothing is used to process the contour of the object in the moving image, and the geometric center values of the two adjacent contour points within the contour range are calculated. According to the calculation results, the curvature angle is further calculated, and the curvature symbol is obtained. According to the curvature symbol, the contour features of the moving image are detected. The experimental results show that the proposed algorithm can effectively and accurately detect the edge contour of the image and shorten the reconstruction time, and the detection image resolution is high.
Highlights
As the edge and contour information detection of multilevel image is the basis of image analysis and processing, the quality of the detection results directly affects the subsequent image quality evaluation, compression, and recognition results, so it is necessary to make a comparative study on the edge and contour detection algorithms of the multilevel image
In [4], a laser image contour detection algorithm based on the maximum interclass variance method is proposed. e laser image is described in the form of two-dimensional gray histogram, and the laser image is segmented by the maximum interclass variance method to obtain the optimal threshold of the laser image target. e optimal threshold is taken as the primary edge of the laser image target, and the edge energy is minimized until the minimum edge energy is obtained. e edge with the minimum energy is the final contour of the laser image target, and the algorithm has high detection accuracy
Conclusion. e results of multilevel image edge contour detection algorithm based on visual perception are close to the sample image, and good image reconstruction results can be obtained in a short time when the sampling rate is lower than 0.5
Summary
Received 3 June 2021; Revised 14 July 2021; Accepted 9 December 2021; Published 10 January 2022. According to formulas (13) and (14), the gradient operator is sensitive to the noise in the multilevel image, so it has poor antinoise ability and cannot effectively detect the edge contour information in different directions in the multilevel image. E reason is that the proposed algorithm sets thresholds for the multilevel image features by analyzing the contour features of the multilevel image, obtains the latest membership function, and completes multithreshold optimization, which is in favor of optimizing the detection results of the sample image to a certain extent. E detection results of the multilevel image edge contour detection algorithm based on visual perception are similar to those of the sample image, and good image reconstruction results can be obtained in a relatively short time when the sampling rate is lower than 0.5. Before the reconstruction of visual images, the variational model and learning dictionary are combined to denoising the images, which eliminates the interference caused by noise on image
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