# Group-based Bi-Directional Recurrent Wavelet Neural Networks for Video Super-Resolution

arXiv: https://arxiv.org/abs/2106.07190 · HTML: https://mlsanity.com/paper/2106.07190/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 33 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [EDVR: Video Restoration with Enhanced Deformable Convolutional Networks](https://mlsanity.com/paper/1905.02716/) (2019)
- [Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution](https://mlsanity.com/paper/1704.03915/) (2017)
- [TDAN: Temporally Deformable Alignment Network for Video Super-Resolution](https://mlsanity.com/paper/1812.02898/) (2018)
- [Deep Back-Projection Networks For Super-Resolution](https://mlsanity.com/paper/1803.02735/) (2018)
- [Frame-Recurrent Video Super-Resolution](https://mlsanity.com/paper/1801.04590/) (2018)
- [Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation](https://mlsanity.com/paper/1611.05250/) (2016)
- [Recurrent Back-Projection Network for Video Super-Resolution](https://mlsanity.com/paper/1903.10128/) (2019)
- [Deep Video Super-Resolution using HR Optical Flow Estimation](https://mlsanity.com/paper/2001.02129/) (2020)

## Abstract (arXiv)

Video super-resolution (VSR) aims to estimate a high-resolution (HR) frame from a low-resolution (LR) frames. The key challenge for VSR lies in the effective exploitation of spatial correlation in an intra-frame and temporal dependency between consecutive frames. However, most of the previous methods treat different types of the spatial features identically and extract spatial and temporal features from the separated modules. It leads to lack of obtaining meaningful information and enhancing the fine details. In VSR, there are three types of temporal modeling frameworks: 2D convolutional neural networks (CNN), 3D CNN, and recurrent neural networks (RNN). Among them, the RNN-based approach is suitable for sequential data. Thus the SR performance can be greatly improved by using the hidden states of adjacent frames. However, at each of time step in a recurrent structure, the RNN-based previous works utilize the neighboring features restrictively. Since the range of accessible motion per time step is narrow, there are still limitations to restore the missing details for dynamic or large motion. In this paper, we propose a group-based bi-directional recurrent wavelet neural networks (GBR-WNN) to exploit the sequential data and spatio-temporal information effectively for VSR. The proposed group-based bi-directional RNN (GBR) temporal modeling framework is built on the well-structured process with the group of pictures (GOP). We propose a temporal wavelet attention (TWA) module, in which attention is adopted for both spatial and temporal features. Experimental results demonstrate that the proposed method achieves superior performance compared with state-of-the-art methods in both of quantitative and qualitative evaluations.