# Real-Time Super-Resolution System of 4K-Video Based on Deep Learning

arXiv: https://arxiv.org/abs/2107.05307 · HTML: https://mlsanity.com/paper/2107.05307/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 12 Jul 2021.

## Builds on (most cited first, 15 of 15)

- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [The Perception-Distortion Tradeoff](https://mlsanity.com/paper/1711.06077/) (2017)
- [EDVR: Video Restoration with Enhanced Deformable Convolutional Networks](https://mlsanity.com/paper/1905.02716/) (2019)
- [Accelerating the Super-Resolution Convolutional Neural Network](https://mlsanity.com/paper/1608.00367/) (2016)
- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [Frame-Recurrent Video Super-Resolution](https://mlsanity.com/paper/1801.04590/) (2018)
- [Deep Learning for Single Image Super-Resolution: A Brief Review](https://mlsanity.com/paper/1808.03344/) (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)
- [Learning for Video Super-Resolution through HR Optical Flow Estimation](https://mlsanity.com/paper/1809.08573/) (2018)
- [Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation](https://mlsanity.com/paper/1811.09393/) (2018)
- [FAST: A Framework to Accelerate Super-Resolution Processing on Compressed Videos](https://mlsanity.com/paper/1603.08968/) (2016)
- [An Energy-Efficient FPGA-based Deconvolutional Neural Networks Accelerator for Single Image Super-Resolution](https://mlsanity.com/paper/1801.05997/) (2018)

## Built on by (most cited first, 7 of 7)

- [Super-NeRF: View-consistent Detail Generation for NeRF super-resolution](https://mlsanity.com/paper/2304.13518/) (2023)
- [Stream-DiffVSR: Low-Latency Streamable Video Super-Resolution via Auto-Regressive Diffusion](https://mlsanity.com/paper/2512.23709/) (2025)
- [Continuous Space-Time Video Super-Resolution with 3D Fourier Fields](https://mlsanity.com/paper/2509.26325/) (2025)
- [Accelerating the Training of Video Super-Resolution Models](https://mlsanity.com/paper/2205.05069/) (2022)
- [SR+Codec: a Benchmark of Super-Resolution for Video Compression Bitrate Reduction](https://mlsanity.com/paper/2305.04844/) (2023)
- [Trajectory-aware Shifted State Space Models for Online Video Super-Resolution](https://mlsanity.com/paper/2508.10453/) (2025)
- [Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects](https://mlsanity.com/paper/2509.22692/) (2025)

## Abstract (arXiv)

Video super-resolution (VSR) technology excels in reconstructing low-quality video, avoiding unpleasant blur effect caused by interpolation-based algorithms. However, vast computation complexity and memory occupation hampers the edge of deplorability and the runtime inference in real-life applications, especially for large-scale VSR task. This paper explores the possibility of real-time VSR system and designs an efficient and generic VSR network, termed EGVSR. The proposed EGVSR is based on spatio-temporal adversarial learning for temporal coherence. In order to pursue faster VSR processing ability up to 4K resolution, this paper tries to choose lightweight network structure and efficient upsampling method to reduce the computation required by EGVSR network under the guarantee of high visual quality. Besides, we implement the batch normalization computation fusion, convolutional acceleration algorithm and other neural network acceleration techniques on the actual hardware platform to optimize the inference process of EGVSR network. Finally, our EGVSR achieves the real-time processing capacity of 4K@29.61FPS. Compared with TecoGAN, the most advanced VSR network at present, we achieve 85.04% reduction of computation density and 7.92x performance speedups. In terms of visual quality, the proposed EGVSR tops the list of most metrics (such as LPIPS, tOF, tLP, etc.) on the public test dataset Vid4 and surpasses other state-of-the-art methods in overall performance score. The source code of this project can be found on https://github.com/Thmen/EGVSR.