The algorithm of Fast REtinA Keypoint (FREAK) descriptor has achieved the rotation invariance via the direction of calculation model, but its matching performance for large change of rotation scale is not ideal and the matching error rate is high. In order to solve the problem, an improved image registration algorithm based on FREAK descriptor was proposed. Firstly, long distance point pairs judged with a given distance threshold, was added to the original FREAK. Only the points of long distance in the keypoint sampling pattern were used to generate angle information. Then, the Hamming distance was weighted. In order to generate descriptor selection point pairs for every key point, the mean of each column of training data descriptors was computed. The mean was closer to 0.5, the weight of the column was larger. This method improved the coarse-calculating state of original Hamming distance and made the distance calculation more accurate. The nearest neighbor matching method combined with the ratio of the nearest neighbor and next nearest neighbor, and the method of RANdom SAmple Consensus (RANSAC) were used for rapid matching and optimization. The experimental results show that, the improved algorithm is more suitable for the applications with large variation of rotation scale and high demand of matching performance.