Recurrent Back-Projection Network For Video Super-Resolution

Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita


We proposed a novel architecture for the problem of video super-resolution. We integrate spatial and temporal contexts from continuous video frames using a recurrent encoder-decoder module, that fuses multi-frame information with the more traditional, single frame super-resolution path for the target frame. In contrast to most prior work where frames are pooled together by stacking or warping, our model, the Recurrent Back-Projection Network (RBPN) treats each context frame as a separate source of information. These sources are combined in an iterative refinement framework inspired by the idea of back-projection in multiple-image super-resolution. This is aided by explicitly representing estimated inter-frame motion with respect to the target, rather than explicitly aligning frames. We propose a new video super-resolution benchmark, allowing evaluation at a larger scale and considering videos in different motion regimes. Experimental results demonstrate that our RBPN is superior to existing methods on several datasets.



Results on 4x




Muhammad Haris, Greg Shakhnarovich, and Norimichi Ukita, "Recurrent Back-Projection Network For Video Super-Resolution", Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.