Improved image edge detection method based on Scharr operator

Abstract

The invention discloses a noisy image edge detection method based on a Scharr operator. The method comprises the following steps: step 1, inputting a noisy image; 2, improving a median filtering valuing method to obtain a denoised image; 3, equalizing the image to obtain a gradient enhanced image; step 4, improving the Scharr edge detection operator to obtain improved edge detection operators of the 45-degree direction template and the 135-degree direction template; step 5, determining edge points to obtain a preliminary edge image; step 6, fusing the edge images to obtain a fused image; and step 7, outputting a final edge detection image. According to the invention, the Scharr operator is improved; a two-direction template of a traditional Scharr operator is expanded to four directions, the image gradient is enhanced through image histogram equalization, the anti-noise capability and the edge detection capability of an image are enhanced through image noise reduction, a proper threshold value is found through image binarization, and the edge detection effect of the image is further improved.

Description

Technical field [0001] The invention relates to an image edge detection method, in particular to an improved image edge detection method based on Scharr operator. Background technique [0002] Edge detection is the most basic technology in image processing and machine vision, and is widely used in image segmentation, image recognition, and image analysis. With the development of technology and the rise of artificial intelligence, edge detection has been applied to face detection, license plate recognition, medical assistance and other technologies. It can be said that edge detection technology is closely related to our lives and plays a role in the image field. Crucial role. [0003] Edge detection technology has always attracted people's attention. Many people with lofty ideals have invested a lot of time in research and proposed many edge detection algorithms, such as Sobel operator, Robert operator, Prewitt operator, LoG operator And Canny operator. Among them, the Sobel oper...

AI Technical Summary

Problems solved by technology

The Canny operator first uses Gaussian filtering on the image to denoise the image, and then uses the first-order partial derivative to perform convolution operations on the horizontal, vertical, and diagonal directions of the image to obtain the brightness gradient map and gradient direction of the image. Moreover, the Canny operator will suppress the non-maximum signal of the image, and finally connect the edges of the image through two thresholds. Although the detection effect is very good, the slow execution speed is also a big problem.

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