ICNet: Joint Alignment and Reconstruction via Iterative Collaboration for Video Super-Resolution

Most previous frameworks either cost too much time or adopt some fixed modules resulting in alignment error in video super-resolution (VSR). In this paper, we propose a novel many-to-many VSR framework with Iterative Collaboration (ICNet), which employs the concurrent operation by iterative collaboration between alignment and reconstruction proving to be more efficient and effective than existing recurrent and sliding-window frameworks. With the proposed iterative collaboration, alignment can be conducted on super-resolved features from reconstruction while accurate alignment boosts reconstruction in return. In each iteration, the features of low-resolution video frames are first fed into the alignment and reconstruction subnetworks, which can generate temporal aligned features and spatial super-resolved features. Then, both outputs are fed into the proposed Tidy Two-stream Fusion (TTF) subnetwork that shares inter-frame temporal information and intra-frame spatial information without redundancy. Moreover, we design the Frequency Separation Reconstruction (FSR) subnetwork to not only model high-frequency and low-frequency information separately but also take benefit of each other for better reconstruction. Extensive experiments on benchmark datasets demonstrate that the proposed ICNet outperforms state-of-the-art VSR methods in terms of PSNR/SSIM values and visual quality, respectively.