Abstract
Existing edge detection algorithms suffer from inefficient edge localization, noise sensitivity, and/or relatively poor automatic detection capability. Contemporary edge detection algorithms can be improved by targeting these problems to help bolster their performance. Grey system theory can be used to resolve the small data and poor information issues in the local information of uncertain systems. An automatic edge detection algorithm was developed in this study based on a grey prediction model to remedy these problems. Noise characteristics in grey images are used to deploy a noise-filtering algorithm based on local features. A mask with twenty-four edge direction information points (345°) was established based on edge line texture features. By compressing the amplitude of the sequence, the randomly oscillated grey prediction sequence can be converted into a smooth, new sequence. The discrete grey model (1,1) (DGM(1,1)) was established based on this new grey prediction sequence to obtain the grey prediction maximum value. A grey prediction image with enhanced edges was obtained by replacing the pixel value in the original image with the maximum grey prediction value. A grey prediction subtraction image with edges separated from non-edge points was also obtained by subtracting the original image from the grey prediction image. The optimal separation threshold in the grey prediction subtraction image can be determined via the global adaptive threshold selection method. The neighborhood search method was then deployed to remove stray points and burrs from the image after the target was separated from the background, creating the final edge image. Experiments were performed on a computer-simulated phantom to find that both the subjective visual effects and objective evaluation criteria are better under the proposed method than several other competitive methods. The proposed edge detection algorithm shows excellent edge detection ability and is highly robust to noise, though the grey prediction model needs further improvement to optimize the run time.
Highlights
The ‘‘edge’’ is the part of a given image where the gray value changes drastically [1], [2]
In order to verify the performance of the proposed algorithm, we compared it to other state-of-the-art algorithms including the improved Canny algorithm proposed by Rong et al [12], the traditional GM (1,1) edge detection algorithm proposed by Wan et al [38], the traditional grey relation analysis (GRA) edge detection algorithm proposed by Li et al [32], the morphological method proposed by Zheng et al [19], and the Zernike moment method proposed by Peng et al [16]
This paper proposes a novel image edge detection algorithm based on the grey prediction model
Summary
The ‘‘edge’’ is the part of a given image where the gray value changes drastically [1], [2]. Z. Zheng et al.: Adaptive Edge Detection Algorithm Based on Improved Grey Prediction Model. Edge detection algorithms based on wavelet transform or neural network methods show better edge localization ability and noise-sensitivity performance, but their calculation burden is so large that the processing time is too long for practical application. Zheng et al [2] proposed an automatic edge detection algorithm based on grey entropy in 2019 which does have excellent edge detection performance, but is prone to double edges and has an overly time-consuming noise filtering algorithm He et al [35] first utilized the grey prediction model for edge detection in 2005; the algorithm can effectively detect the edges of a given image but it is sensitive to noise. An adaptive edge detection algorithm based on an improved grey prediction model was developed in this study. Const, Eq (11) is written by combining odd and even cases; this is the improved grey prediction model used in this study
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