Abstract:  Image matching is the key technology in augmented reality system，and matching accuracy is the key to improving the performance of feature matching.A multi-scale feature matching enhancement algorithm（I-AKAZE） is proposed.By improving the conduction function in the process of nonlinear anisotropic filtering，the nonlinear diffusion speed in the region with large gradient value of the image is slowed down，and the edge features of the matched image are retained to a great extent.At the same time，combined with the improved nonlinear quantization accelerated robust feature descriptor（NLG-SURF），the recognition rate of the descriptor is improved.The experimental results show that the repeatability score of I-AKAZE algorithm on Mikolajczyk data set is greatly improved compared with the current advanced AKAZE algorithm，that the average recognition rate of the corresponding feature descriptors is increased by 8.4%，and that the running speed is about 600ms faster than that of the classic SIFT algorithm.The overall performance of the algorithm is improved in the detection and description stages.

Key words:  , feature detection；feature descriptor；nonlinear filtering；scale space；conduction function