Fine feature matching in image stitching and blurred panorama areas is time consuming due to inaccurate image registration. To mitigate this issue, this study proposes a new image registration model based on improved grid motion statistics and weighted projection transformation. The method uses the oriented fast and rotated BRIEF (ORB) algorithm to extract and describe the image features. The brute-force matching algorithm is used for rough image matching. The image is divided into multiple square grids. Then, these grid features are counted and five grids feature scores are calculated to eliminate error matching and obtain the refined matching feature set. Lastly, image registration is achieved by adding a distance weighting coefficient to construct the weighted projection transformation model. Comparing the proposed algorithm with other methods used in the stitching sequence set, the experimental results revealed that the accuracy of the proposed algorithm was improved by an average of 28.7% for image registration and the feature matching speed was improved by 43.6%. The stitched panoramic image did not show any obvious geometrical dislocation or distortion, and the overall imaging appears quite natural.