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http://dx.doi.org/10.5351/KJAS.2020.36.6.813

An effective edge detection method for noise images based on linear model and standard deviation  

Park, Youngho (Department of Big Data Application, Hannam University)
Publication Information
The Korean Journal of Applied Statistics / v.33, no.6, 2020 , pp. 813-821 More about this Journal
Abstract
Recently, research using unstructured data such as images and videos has been actively conducted in various fields. Edge detection is one of the most useful image enhancement techniques to improve the quality of the image process. However, it is very difficult to perform edge detection in noise images because the edges and noise having high frequency components. This paper uses a linear model and standard deviation as an effective edge detection method for noise images. The edge is detected by the difference between the standard deviation of the pixels included in the pixel block and the standard deviation of the residual obtained by fitting the linear model. The results of edge detection are compared with the results of the Sobel edge detector. In the original image, the Sobel edge detection result and the proposed edge detection result are similar. Proposed method was confirmed that the edge with reduced noise was detected in the various levels of noise images.
Keywords
edge detector; linear model; standard deviation; noise reduction; statistical image processing;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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