# Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution

arXiv: https://arxiv.org/abs/1806.05764 · HTML: https://mlsanity.com/paper/1806.05764/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2018.

## Built on by (most cited first, 30 of 30)

- [Rethinking Alignment in Video Super-Resolution Transformers](https://mlsanity.com/paper/2207.08494/) (2022)
- [Real-World Single Image Super-Resolution: A Brief Review](https://mlsanity.com/paper/2103.02368/) (2021)
- [Reference-based Image Super-Resolution with Deformable Attention Transformer](https://mlsanity.com/paper/2207.11938/) (2022)
- [Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors](https://mlsanity.com/paper/2205.14886/) (2022)
- [DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-Resolution](https://mlsanity.com/paper/2505.16239/) (2025)
- [Fair Generative Models via Transfer Learning](https://mlsanity.com/paper/2212.00926/) (2022)
- [Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution](https://mlsanity.com/paper/2210.16836/) (2022)
- [Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution Layers](https://mlsanity.com/paper/2305.17313/) (2023)
- [Improved Adversarial Diffusion Compression for Real-World Video Super-Resolution](https://mlsanity.com/paper/2603.00458/) (2026)
- [Optical Flow for Video Super-Resolution: A Survey](https://mlsanity.com/paper/2203.10462/) (2022)
- [Identifying and Mitigating Flaws of Deep Perceptual Similarity Metrics](https://mlsanity.com/paper/2207.02512/) (2022)
- [DiTVR: Zero-Shot Diffusion Transformer for Video Restoration](https://mlsanity.com/paper/2508.07811/) (2025)
- [DUO-VSR: Dual-Stream Distillation for One-Step Video Super-Resolution](https://mlsanity.com/paper/2603.22271/) (2026)
- [A generalised novel loss function for computational fluid dynamics](https://mlsanity.com/paper/2411.17059/) (2024)
- [MedVSR: Medical Video Super-Resolution with Cross State-Space Propagation](https://mlsanity.com/paper/2509.21265/) (2025)
- [Asymmetric VAE for One-Step Video Super-Resolution Acceleration](https://mlsanity.com/paper/2509.24142/) (2025)
- [Enhancing Space-time Video Super-resolution via Spatial-temporal Feature Interaction](https://mlsanity.com/paper/2207.08960/) (2022)
- [A Practical Framework for Unsupervised Structure Preservation Medical Image Enhancement](https://mlsanity.com/paper/2304.01864/) (2023)
- [Survey on Visual Signal Coding and Processing with Generative Models: Technologies, Standards and Optimization](https://mlsanity.com/paper/2405.14221/) (2024)
- [QuantVSR: Low-Bit Post-Training Quantization for Real-World Video Super-Resolution](https://mlsanity.com/paper/2508.04485/) (2025)
- [Towards Redundancy Reduction in Diffusion Models for Efficient Video Super-Resolution](https://mlsanity.com/paper/2509.23980/) (2025)
- [Identity Card Presentation Attack Detection: A Systematic Review](https://mlsanity.com/paper/2511.06056/) (2025)
- [DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel UNet for enhancing super-resolution of dynamic MRI](https://mlsanity.com/paper/2202.05355/) (2022)
- [Reference-based OCT Angiogram Super-resolution with Learnable Texture Generation](https://mlsanity.com/paper/2305.05835/) (2023)
- [Omnidirectional Video Super-Resolution using Deep Learning](https://mlsanity.com/paper/2506.14803/) (2025)
- [LARNet: Latent Action Representation for Human Action Synthesis](https://mlsanity.com/paper/2110.10899/) (2021)
- [SuperTran: Reference Based Video Transformer for Enhancing Low Bitrate Streams in Real Time](https://mlsanity.com/paper/2211.12604/) (2022)
- [RBPGAN: Recurrent Back-Projection GAN for Video Super Resolution](https://mlsanity.com/paper/2311.09178/) (2023)
- [HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models](https://mlsanity.com/paper/2401.05870/) (2024)
- [General Intelligent Imaging and Uncertainty Quantification by Deterministic Diffusion Model](https://mlsanity.com/paper/2408.13061/) (2024)

## Abstract (arXiv)

Video super-resolution (VSR) has become one of the most critical problems in video processing. In the deep learning literature, recent works have shown the benefits of using adversarial-based and perceptual losses to improve the performance on various image restoration tasks; however, these have yet to be applied for video super-resolution. In this work, we propose a Generative Adversarial Network(GAN)-based formulation for VSR. We introduce a new generator network optimized for the VSR problem, named VSRResNet, along with a new discriminator architecture to properly guide VSRResNet during the GAN training. We further enhance our VSR GAN formulation with two regularizers, a distance loss in feature-space and pixel-space, to obtain our final VSRResFeatGAN model. We show that pre-training our generator with the Mean-Squared-Error loss only quantitatively surpasses the current state-of-the-art VSR models. Finally, we employ the PercepDist metric (Zhang et al., 2018) to compare state-of-the-art VSR models. We show that this metric more accurately evaluates the perceptual quality of SR solutions obtained from neural networks, compared with the commonly used PSNR/SSIM metrics. Finally, we show that our proposed model, the VSRResFeatGAN model, outperforms current state-of-the-art SR models, both quantitatively and qualitatively.