融合扩散模型和流形梯度约束的无人机图像拼接方法*

doi:10.7523/j.ucas.2024.061

摘要/Abstract

摘要： 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,以至于影响后续分析和应用。现有方法通常不能同时解决这两个问题,需要对图像拼接结果进行多次处理,造成多次误差传递。本文提出了一种基于去噪离散扩散模型(denoising diffusion probability model, 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来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,实验结果表明本文方法有效消除了拼接结果中的不规则边界与拼接缝,修复前后从局部到全局图像质量评价(from patches to pictures, PaQ-2-PiQ)与多尺度图像质量转换评价模型(multi-scale image quality transformer, MUSIQ)得分分别提升了4.36%和15.37%,修复后不规则边界处的结构相似性（structural similarity, SSIM）和峰值信噪比（peak signal-to-noise ratio, PSNR）值分别提升了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,本文方法在主观和客观两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。

关键词: 无人机图像, 扩散模型, 图像拼接, 不规则边界, 拼接缝

Abstract: Image stitching is a crucial prerequisite step for unmanned aerial vehicle (UAV) remote sensing applications, while the stitched images using most of current image stitching methods often suffer 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 cannot often 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 doAAAing 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. Comparative experiments leveraging data from diverse scenarios were conducted. The experimental results demonstrated the efficacy of the proposed method in effectively eliminating irregular boundaries and seams in the image stitching. Moreover, the patches quality to pictures quality (PaQ-2-PiQ) and multi-scale image quality (MUSIQ) scores were improved 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 20.22% and 33.69%, respectively, with the proposed restoration. Compared with state-of-the-art methods and other conventional image stitching algorithms, the proposed method performs better than other methods in both subjective and objective quality metric scores, has good robustness and generalization, and can be widely applied to UAV image stitching scenarios.

Key words: UAV image, diffusion model, image stitching, irregular boundaries, stitching seams

中图分类号:

TP751.1

王杰, 罗永曦, 陈俊, 吴业炜. 融合扩散模型和流形梯度约束的无人机图像拼接方法^*[J]. 中国科学院大学学报, DOI: 10.7523/j.ucas.2024.061.

WANG Jie, LUO Yongxi, CHEN Jun, WU Yewei. UAV image stitching method based on diffusion model and manifold gradient constraint[J]. Journal of University of Chinese Academy of Sciences, DOI: 10.7523/j.ucas.2024.061.

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融合扩散模型和流形梯度约束的无人机图像拼接方法^*

UAV image stitching method based on diffusion model and manifold gradient constraint

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