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. 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. 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. 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.