In this study, a power function-weighted image stitching method with fusion-improved SURF (Speeded Up Robust Feature) and Cell acceleration is proposed to resolve problems, such as the low feature point matching accuracy associated with the traditional algorithms in the image stitching process and ghosting, color difference, and stitching gaps observed during the image fusion process. First, the similarity of the feature points is verified using cosine similarity. Then, the two-way consensus algorithm and the MSAC algorithm are combined to finely match the rough matching points. Finally, the power function weights obtained via cell acceleration are used to fuse images for obtaining the image stitching. Experimental results show that compared with other algorithms, the feature point matching accuracy of the proposed algorithm increases by approximately 11%, the mean square error decreases by approximately 1.32%-1.48%, the information entropy increases by approximately 0.98%-1.70%, and the total stitching time decreases by approximately 2 s. Compared with other algorithms, the proposed algorithm obtains better results with respect to the matching accuracy and fusion effect; furthermore, improved image splicing quality and universality can be obtained.