In optical remote sensing image reconstruction, image registration is an important issue to address in order to ensure satisfactory reconstruction performance. In this study, a multi-frame image registration algorithm for high-resolution images and its parallel design method are proposed. The algorithm realizes an improved feature point detection method based on an adaptive gradient bilateral tensor filter and carries out weighted Gaussian surface sub-pixel interpolation to obtain more accurate corner positions, which better guarantees the registration accuracy. On this basis, multi-scale expansion is carried out to generate descriptors for image registration. In addition, the operation-level parallel analysis and design are carried out on a GPU platform based on compute unified device architecture (CUDA), and the memory model of the GPU is utilized reasonably. The task-level parallel analysis and design are carried out based on the GPU stream model. Moreover, based on the open multi-processing (OpenMP) platform, a multi-core CPU carries out parallel design at the operation level and task level, which realizes post-processing operations such as optical remote sensing images loading, accurate matching, and coordinate mapping, thereby effectively improving registration speed. Compared with feature point algorithms and deep learning algorithm, our algorithm and its parallel design significantly improve the registration accuracy and speed of high-resolution optical remote sensing images.

中文翻译：

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高分辨率光学遥感影像的高精度配准算法及并行设计方法

在光学遥感图像重建中，图像配准是一个重要的问题，以确保令人满意的重建性能。在这项研究中，提出了一种高分辨率图像的多帧图像配准算法及其并行设计方法。该算法实现了一种基于自适应梯度双边张量滤波器的改进的特征点检测方法，并进行加权高斯表面亚像素插值以获得更准确的角点位置，更好地保证了配准精度。在此基础上进行多尺度扩展，生成用于图像配准的描述符。此外，在基于计算统一设备架构（CUDA）的GPU平台上进行操作级并行分析设计，合理利用GPU的内存模型。基于GPU流模型进行任务级并行分析设计。此外，基于开放式多处理（OpenMP）平台，多核CPU在操作级和任务级进行并行设计，实现光学遥感影像加载、精准匹配、坐标等后处理操作。映射，从而有效提高配准速度。与特征点算法和深度学习算法相比，我们的算法及其并行设计显着提高了高分辨率光学遥感图像的配准精度和速度。多核CPU在操作层和任务层进行并行设计，实现光学遥感影像加载、精准匹配、坐标映射等后处理操作，有效提高配准速度。与特征点算法和深度学习算法相比，我们的算法及其并行设计显着提高了高分辨率光学遥感图像的配准精度和速度。多核CPU在操作层和任务层进行并行设计，实现光学遥感影像加载、精准匹配、坐标映射等后处理操作，有效提高配准速度。与特征点算法和深度学习算法相比，我们的算法及其并行设计显着提高了高分辨率光学遥感图像的配准精度和速度。