A Multi-Frame Super-Resolution Reconstruction Method Based on Optical Flow for Space Noncooperative Objects

With the continuous advancement of space technology, the number of defunct spacecraft, abandoned rocket bodies, and debris in space is increasing. These non-cooperative objects occupy a significant amount of orbital resources and pose a substantial threat to the safety of on-orbit spacecraft. This paper focuses on close-proximity operations in space and aims to address the limitation of camera resolution by proposing an optical flow-based multi-frame super-resolution reconstruction algorithm. This algorithm employs a multi-level wavelet convolutional network (MWCNN) for feature extraction and uses SpyNet to obtain multi-level optical flow between different frames. The multi-level optical flow pyramid alignment network is used to align features, and a recurrent network is utilized for frame-by-frame feature fusion. Finally, a reconstruction network generates high-resolution images. Extensive experiments have demonstrated that our proposed method effectively enhances the perception capabilities of space non-cooperative objects.