ULSR-UV: an ultra-lightweight super-resolution networks for UAV video

Existing lightweight video super-resolution network architectures are often simple in structure and lack generalization ability when dealing with complex and varied real scenes in aerial videos of unmanned aerial vehicle. Furthermore, these networks may cause issues such as the checkerboard effect and loss of texture information when processing drone videos. To address these challenges, we propose a super-lightweight video super-resolution reconstruction network based on convolutional pyramids and progressive residual blocks: ULSR-UV. The ULSR-UV network significantly reduces model redundancy and achieves high levels of lightness by incorporating a 3D lightweight spatial pyramid structure and more efficient residual block designs. This network utilizes a specific optimizer to efficiently process drone videos from both multi-frame and single-frame dimensions. Additionally, the ULSR-UV network incorporates a multidimensional feature loss calculation module that enhances network performance and significantly improves the reconstruction quality of drone aerial videos. Extensive experimental verification has demonstrated ULSR-UV's outstanding performance in the field of drone video super-resolution reconstruction.