目的 无人机摄像资料的分辨率直接影响目标识别与信息获取,所以摄像分辨率的提高具有重大意义。为了改善无人机侦察视频质量,针对目前无人机摄像、照相数据的特点,提出一种无人机侦察视频超分辨率重建方法。方法 首先提出基于AGAST-Difference与Fast Retina Keypoint (FREAK)的特征匹配算法对视频目标帧与相邻帧之间配准,然后提出匹配区域搜索方法找到目标帧与航片的对应关系,利用航片对视频帧进行高频补偿,最后采用凸集投影方法对补偿后视频帧进行迭代优化。结果 基于AGAST-Difference与FREAK的特征匹配算法在尺度、旋转、视点等变化及运行速度上存在很大优势,匹配区域搜索方法使无人机视频的高频补偿连续性更好,凸集投影迭代优化提高了重建的边缘保持能力,与一种简单有效的视频序列超分辨率复原算法相比,本文算法重建质量提高约4 dB,运行速度提高约5倍。结论 提出了一种针对无人机的视频超分辨率重建方法,分析了无人机视频超分辨率问题的核心所在,并且提出基于AGAST-Difference与FREAK的特征匹配算法与匹配区域搜索方法来解决图像配准与高频补偿问题。实验结果表明,本文算法强化了重建图像的一致性与保真度,特别是对图像边缘细节部分等效果极为明显,且处理速度更快。

Objective The resolution of unmanned aerial vehicle (UAV) video has a direct effect on target recognition and information acquisition, thereby playing a highly significant role in improving video resolution. Currently, super-resolution reconstruction for UAV is proposed to improve the quality of UAV reconnaissance video for the characteristics of the UAV camera and camera data. Method The feature matching algorithm based on AGAST-Difference and Fast Retina Keypoint (FREAK) is primarily proposed to match the video object frame and the adjacent frames. Then, the matching region search method is proposed to find the corresponding relationship between the target frame and the aerial image, and aerial photographs are used to make high-frequency compensation of video frame. Finally, solving the optimization of video compensated by the proposed iteration steps utilizes the Projection Onto Convex Sets (POCS) method. Result Experimental results show that the feature matching algorithm based on AGAST-Difference and FREAK has significant advantages in scale, rotation, and viewpoint. The matching region search method improves the high-frequency compensation continuity of UAV video. POCS iterative optimization improves the reconstruction capability of edge preservation. Compared with the algorithm presented in A Simple & Effective Video Sequence Super-Resolution Algorithm, the algorithm in this study is approximately five times faster, and its image reconstruction is improved by approximately 4 dB. Conclusion In this study, super-resolution reconstruction for UAV video is presented, and the core of the UAV video super-resolution is analyzed. The feature matching algorithm based on AGAST-Difference and FREAK as well as the matching region search method are proposed to solve problems of image registration and high-frequency compensation. The experimental results show that the consistency and fidelity of the reconstructed image are enhanced, the effect of the image edge detail is especially extremely obvious, and the processing speed is fast.