Recurrent Back-Projection Network for Video Super-Resolution
This addresses video quality enhancement for applications like streaming or surveillance, representing an incremental improvement with a novel architectural approach.
The paper tackles video super-resolution by proposing a Recurrent Back-Projection Network (RBPN) that integrates spatial and temporal contexts from multiple frames without explicit alignment, and it demonstrates superior performance over existing methods on several datasets.
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.