Objective In view of the current featu re extraction and matching of fuzzy images,there are defects such as difficulty in feature extraction,low matching rate,weak anti-noise and anti-scale changes .Methods A matching algorithm with high accuracy and high feature recognition rate based on the combination of SIFT algorithm and improved center symmetric local binary pattern is proposed.First, SIFT is used for feature extraction to generate multidimensional descriptors.Second,the improved center s ymmetric local binary mode is used to reduce the dimension of high-dimensional feature descriptors,and local feature regions are used to feature the reduced-dimensional descriptors.Detect and generate texture feature images and information distribution histograms,perform information statistics on feature points in feature areas,and set detection thresholds.Extract feature points that meet the requirements of feature information,and implement rough image matching based on Hausdorff distance algorithm,and finally use RANSAC algorithm to eliminate erro r matching to improve the accuracy and robustness of matching.Results The test results show that the algo rithm proposed in this paper is effective.It not only has good fuzzy image resolution and anti-scale change ch aracteristics,but also has strong noise suppression and anti-light changes.Conclusion The robust feature matchin g algorithm based on visual blur proposed in this paper not only considers the advantages and disadvantages of tr aditional feature matching algorithms,but also proposes new ideas for algorithm improvement,and has obvious stability and accuracy compared with SIFT and LBP algorithms improve.