Adaptive Edge Detection Algorithm Based on Improved Grey Prediction Model

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.

Algorithmic Approaches For Image Edge Detection

“…After that, the proposed algorithm converts the image into binary image and move to next step. After the noise removal and resizing the algorithm apply spatial filter [19].…”

Sobel Edge Detection Based on Weighted Nuclear Norm Minimization Image Denoising

“…The edge of an image is some continuous pixels in the image that have drastic changes in grayscale [10]. From the feature that the pixel at the edge of the image has the largest grayscale jump, the following information can be obtained: along the edge direction, the grayscale value changes slowly; perpendicular to the edge direction, the grayscale value jumps significantly.…”

Quality Assessment Methods to Evaluate the Performance of Edge Detection Algorithms for Digital Image: A Systematic Literature Review

“…Zheng et al [40] have used a measure called a continuity evaluation index in their work. The continuity index ρ is defined as:…”

“…Sun et al [19] generate artificial images to represent hyperspectral images by using HYDRA [20]. Chen [29] and Zheng et al [40]. BSDS500 provides 200 images for training, 100 images for validation, and 200 images for testing.…”

“…The aim is to produce edges with better connectivity. 8) Zheng et al [40] in the year 2020, which is using BSDS500. The work was on developing an adaptive edge detection algorithm.…”