Subpixel edge detection algorithm based on improved Gaussian fitting and Canny operator

In image processing, image edge is often used as a basic feature in higher-level image processing. Edge detection technology is the basis of image processing technologies such as image measurement, image segmentation, image compression and pattern recognition. It is one of the important research topics in digital image processing. In this paper, we explore the image edge and pixel data, and propose an optimization model for sub-pixel edge extraction, image distortion correction and image edge segmentation. We preprocess the image with Gaussian filter, median filter and morphological close operation to reduce the impact of lighting environment and noise on the image. After edge detection with Canny operator, we use two-dimensional interpolation to Gaussian fit the edge points in the gradient direction to obtain the sub-pixel edge, and then search the pixel points to obtain the pixel order of the graphic outline and display it with different colors. Finally, we summarize the model, adjust the search range and extend it to the case of low definition of the original image. We can obtain high-precision contour edges of low pixels through technical means.