Papers › Recurrent Back-Projection Network for Video Super-Resolution

Recurrent Back-Projection Network for Video Super-Resolution

25 Mar 2019CVPR 2019 6arXiv:1903.10128archive 2025-07-28

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.

Code

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Methods

Introduced by this paper: RBPN

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