针对图像拼接过程中传统算法存在特征点匹配正确率低和图像融合过程中出现重影、色差及拼接缝隙等问题,提出一种融合改进SURF(Speeded Up Robust Feature)和Cell加速的幂函数加权图像拼接方法。首先利用余弦相似度初步判断特征点的相似性,然后结合双向一致性算法和MSAC算法对粗匹配点进行精匹配,最后使用Cell加速的幂函数权重对图像进行融合,从而完成图像拼接。实验结果表明,相比于其他算法,所提算法的特征点匹配正确率高出约为11个百分点,均方误差缩小约为1.32%~1.48%,信息熵提升约为0.98%~1.70%,拼接总时间消耗减少约为2 s。所提算法在匹配正确率和融合效果上有较好的效果,且同时拥有较好的拼接图像质量,具有更好的普适性。

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.