To improve the speed and precision of registration in image stitching, this study proposes a modified RANSAC algorithm based on Determinantal Point Processes (DPP), aiming to tackle the issue of robustness model estimation. This method utilizes global negative correlation of the DPP sampling to model matching feature points, eliminates those incorrect matching points, and therefore realizes the homogenization and decentralization of the sampling. The point set extracted in DPP is used as the input of RANSAC to elicit transformation matrix. Experimental results show that compared with traditional RANSAC algorithm, this algorithm ensures higher accuracy and robustness, which greatly enhances the efficiency of automatic image stitching.