Recurrent Back-Projection Network for Video Super-Resolution

Code

• github.com/alterzero/RBPN-PyTorch
  pytorch★ 0
• github.com/MindSpore-paper-code-2/code2/tree/main/rbpn
  mindspore★ 0
• github.com/2023-MindSpore-1/ms-code-216/tree/main/rbpn
  mindspore★ 0
• github.com/zhangtianmingxp/rbpn_mindspore
  mindspore★ 0
• github.com/Mind23-2/MindCode-5/tree/main/rbpn
  mindspore★ 0
• github.com/code-implementation1/Code6/tree/main/ras
  mindspore★ 0
• github.com/xiuyu0000/new_papers_codes/tree/main/rbpn
  mindspore★ 0

Abstract

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.

Tasks

Benchmark Results

Reproductions