In the process of vision-based autonomous UAV navigation, accurate identification of waypoints was the key to guiding the UAV to fly accurately toward the waypoint. However, when the UAV reached the waypoint recognition distance, the airborne image sensor was often affected by weather factors, defocusing, diffraction and other phenomena in the imaging process, which often resulted in blurred images and low spatial resolution. Thus, the accuracy of subsequent waypoint recognition was directly affected. Aiming at this problem, an aerial image super-resolution reconstruction algorithm with improved sparse representation regularization was proposed. Firstly, based on the sparse representation regularization framework, the regularization term of the objective function was constructed by using auto-regressive and non-local similarity constraints; Secondly, according to the characteristics of the image local variance that can effectively distinguish the edge area and the smooth area of the image, the regularization parameters were selected to obtain the objective function in the super-resolution reconstruction model; Finally, the Majorization-Minorization algorithm was used to solve the convex optimization problem of the objective function. Experimental results show that compared with the traditional regularized SR reconstruction algorithm, the proposed algorithm can effectively improve the spatial resolution of images, so that the reconstructed image contains more feature detail information, which provides help for waypoint recognition.