基于改进稀疏表示正则化的SR重建算法

基于视觉的无人机自主导航过程中，对航路点进行准确识别是引导无人机朝着航路点方向精确飞行的关键。然而，当无人机到达航路点识别距离后，由于机载图像传感器受天气因素及成像过程中的脱焦、衍射等现象影响，常导致获取到的航拍图像模糊、空间分辨率较低，从而直接影响了后续航路点识别的精度。针对这一问题，提出了一种改进稀疏表示正则化的航拍图像超分辨率重建算法。首先，基于稀疏表示正则化框架，利用自回归和非局部相似约束构建目标函数的正则化项；其次，根据图像局部方差能有效区分图像的边缘区域和平滑区域这一特性，自适应地选取正则化参数得到超分辨率重建模型中的目标函数；最后，使用MM (Majorization-Minorization) 算法求解目标函数的凸优化问题，得到重建后的高分辨率图像。实验结果表明:与传统的正则化SR重建算法相比，文中算法能够有效的提高航拍图像的空间分辨率，使得重建后的图像包含了更多的特征细节信息，这为航路点识别提供了帮助。

SR reconstruction algorithm of regularization based on improve of sparse representation

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.