Joint Multiframe Superresolution Reconstruction Approach for Video Imaging Based on Information Metasurfaces

Information metasurfaces offer a low-cost solution for microwave forward-looking imaging. However, imaging dynamic and non-sparse scenes using information metasurfaces remains challenging. To address this issue, we present a joint multi-frame super-resolution reconstruction approach for video imaging using information metasurfaces. By leveraging the ultra-fast code-switching capability of information metasurfaces, we formulate the video imaging problem as a joint multi-frame super-resolution reconstruction task. Furthermore, by exploiting the temporal correlation of the background and the sparsity of dynamic variation across multiple frames, we introduce a low-rank and sparse decomposition with inter-frame smoothness model for the super-resolution reconstruction. An effective algorithm based on the proximal gradient method is developed to solve the proposed model, enabling high-resolution imaging of dynamic and non-sparse scenes. Simulation results demonstrate that the proposed approach achieves superior reconstruction performance compared to the existing computational imaging methods.