针对图像拼接任务中出现的图像误匹配特征点较多导致的拼接时间较长、直接使用全部特征点存在的图像拼接精度不够等问题，提出了一种基于匹配点递增策略与随机抽样一致（random sample consensus, RANSAC）相结合的图像拼接优化方法。该方法首先通过特征点初筛选避免大量无效抽样以提高计算效率，接着使用一种渐进的采样策略逐步增加匹配点并反复采样获得精确结果，最后采用一种新的基于均方根误差的损失函数筛选结果，得出最优模型。实验结果表明，在未明显增加耗时的情况下，本文算法的内点率进一步提高，特征点误差均值与均方根误差有了明显下降，图像拼接的精度提高，有效改善了拼接缝处的错位现象，显著减少了图像拼接任务中拼接误差。

To address the issues of long stitching time due to numerous mismatched feature points and insufficient stitching accuracy when using all feature points directly in image stitching tasks, an optimized image stitching method combining a matching point increasing strategy with RANSAC(random sample consensus) was proposed. The method initially screened feature points to prevent numerous ineffective samples, thus improving computational efficiency. Then, a progressive sampling strategy was employed to incrementally increase matching points and repeatedly sample for precise results. Finally, the optimal model was obtained by utilizing a new loss function based on root mean square error to filter the results. The experimental results indicate that, without a noticeable increase in time consumption, the interior point rate of the algorithm in this paper is further enhanced, the mean and root mean square errors of feature points have decreased significantly, the accuracy of image stitching is improved, the misalignment phenomenon at the stitching seam is effectively improved, and the stitching errors in image stitching tasks are significantly reduced.