Abstract: Accurate removal of the skeletal burrs is the most critical step in the extraction of interference fringe skeleton, which can be applied in laser interference fringe detection. A burrs removal algorithm of interference fringe skeleton based on skeleton features is proposed,which includes the acquisition of feature points of skeleton and the tracking of the eight-neighborhood linked list. First, the pixel points are scanned one by one to obtain the four feature points of the skeleton: endpoints, nodes, glitch points, and backbone points. Then, the algorithm of eight neighborhood linked list based on feature points is used to extract all the glitch points and backbone points, and the difference operation was performed based on nodes to remove the burrs. Finally, the processed image is iterated until the interference fringe skeleton burrs are completely removed. The OpenCV machine vision algorithm was used to simulate the burrs image removal, the results were verified by 1 000 pieces of burrs images, and the correct rate of burrs removal is 94%. Compared with the traditional scheme, the proposed algorithm has a higher pertinence, retains the backbone of the skeleton, and removes the remaining burrs, which has a broad application prospect in interference fringe detection.