无人机图像拼接是无人机遥感应用的必要前置步骤，但拼接结果中通常会存在大块不规则边界和多个拼接缝，影响后续分析和应用。现有方法通常不能同时解决这2个问题，需要对图像拼接结果进行多次处理，造成多次误差传递。针对这一问题，提出一种基于去噪离散扩散模型（DDPM）的无人机图像拼接补全方法，该方法对不规则边界和拼接缝统一设计掩码，利用带有流形梯度先验约束的扩散模型补全掩码区域的图像，可同时消除不规则边界与拼接缝，改进拼接结果的质量。利用不同场景数据开展对比实验，结果表明该方法有效消除了拼接结果中的不规则边界与拼接缝，修复后从局部到全局图像质量评价（PaQ-2-PiQ）与多尺度图像质量转换评价模型（MUSIQ）得分分别提升4.36%和15.37%，不规则边界处的结构相似性（SSIM）和峰值信噪比（PSNR）分别提升20.22%与33.69%。与SOTA方法和其他经典的图像拼接算法的对比实验结果表明，该方法在主观和客观2种质量评价方式下都优于其他方法，具有良好的鲁棒性和泛化性，可广泛应用于无人机图像拼接场景。

Image stitching is a crucial prerequisite step for unmanned aerial vehicle （UAV） remote sensing applications， while the stitched images using most of the current image stitching methods often suffer from large irregular boundaries and multiple stitching seams， which can seriously affect subsequent analysis and applications. Existing improved methods typically cannot simultaneously address these two issues， and integrating the two types of methods in sequence is a straightforward way to solve the two problems， while this often can not obtain satisfactory performance because of the inevitable error propagation problem. This paper proposes an inpainting method for the UAV image stitching task based on the denoising diffusion probability model （DDPM）. The method uniformly designs masks for irregular boundaries and stitching seams， and a diffusion model is then utilized with manifold gradient prior constraints to complete the masked regions. By doing so， both irregular boundaries and stitching seams are simultaneously eliminated， thereby improving the quality of the stitching results. Comparative experiments are conducted using four datasets established for different scenarios. The experimental results demonstrated the efficacy of the proposed method in effectively eliminating irregular boundaries and seams in the image stitching. Moreover， from patches to pictures quality （PaQ-2-PiQ） and multi-scale image quality （MUSIQ） scores increased by 4.36% and 15.37%， respectively. Furthermore， at the locations of irregular boundaries， the structural similarity （SSIM） and peak signal-to-noise ratio （PSNR） values improved by 20.22% and 33.69%， respectively. Compared with state-of-the-art methods and other conventional image stitching algorithms， the proposed method performs better in both subjective and objective quality metric scores， has good robustness and generalization， and can be widely applied to UAV image stitching scenarios.