An improved SURF algorithm based on gradient and amplitude pre-computation

This paper presents an improved SURF algorithm based on gradient and amplitude pre-computation. This method is based on the traditional SURF algorithm and replaces the complex Gauss calculation with integral graph search. There are extreme value features and high frequency noise describing the edge and sharpness of the image. In order to measure the relationship between them, it is necessary to introduce the signal-to-noise ratio measurement index to highlight the effective features or their components. When constructing Hessian matrix with SURF algorithm, Hessian algorithm is sensitive to gradient change. It can describe the gradient of image well. The method of calculating gradient magnitude before Hessian can effectively improve the effect of existing SURF algorithm. This method solves the problem of small number of feature points and non-uniform feature points in traditional SURF algorithm. It has the advantages of high accuracy of feature points extraction and better noise suppression. It can be introduced into underwater three-dimensional reconstruction, which can effectively improve the accuracy and quality of underwater target three-dimensional reconstruction and provide strong support for underwater observation and operation of underwater vehicles.