# A Practical Contrastive Learning Framework for Single-Image Super-Resolution

arXiv: https://arxiv.org/abs/2111.13924 · HTML: https://mlsanity.com/paper/2111.13924/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 60 of its 85 references that are in the map.
- First posted on arXiv 27 Nov 2021.

## Builds on (most cited first, 30 of 60)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [What Makes for Good Views for Contrastive Learning?](https://mlsanity.com/paper/2005.10243/) (2020)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [Contrastive Representation Distillation](https://mlsanity.com/paper/1910.10699/) (2019)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Contrastive Learning with Hard Negative Samples](https://mlsanity.com/paper/2010.04592/) (2020)
- [Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution](https://mlsanity.com/paper/1704.03915/) (2017)

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

- [A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends](https://mlsanity.com/paper/2301.05712/) (2023)
- [A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends](https://mlsanity.com/paper/2410.15067/) (2024)
- [ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution](https://mlsanity.com/paper/2307.00781/) (2023)
- [A Unified Framework for Microscopy Defocus Deblur with Multi-Pyramid Transformer and Contrastive Learning](https://mlsanity.com/paper/2403.02611/) (2024)
- [Spectral Normalization and Dual Contrastive Regularization for Image-to-Image Translation](https://mlsanity.com/paper/2304.11319/) (2023)
- [Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration](https://mlsanity.com/paper/2309.06023/) (2023)
- [Learning from Multi-Perception Features for Real-Word Image Super-resolution](https://mlsanity.com/paper/2305.18547/) (2023)
- [CtxMIM: Context-Enhanced Masked Image Modeling for Remote Sensing Image Understanding](https://mlsanity.com/paper/2310.00022/) (2023)
- [Real-World Light Field Image Super-Resolution via Degradation Modulation](https://mlsanity.com/paper/2206.06214/) (2022)
- [MRI Super-Resolution with Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2511.16854/) (2025)
- [Uncovering the Over-smoothing Challenge in Image Super-Resolution: Entropy-based Quantification and Contrastive Optimization](https://mlsanity.com/paper/2201.01034/) (2022)
- [Fully 1×1 Convolutional Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2307.16140/) (2023)
- [Fine Tuning without Catastrophic Forgetting via Selective Low Rank Adaptation](https://mlsanity.com/paper/2501.15377/) (2025)
- [Beyond Degradation Redundancy: Contrastive Prompt Learning for All-in-One Image Restoration](https://mlsanity.com/paper/2504.09973/) (2025)
- [CoReFusion: Contrastive Regularized Fusion for Guided Thermal Super-Resolution](https://mlsanity.com/paper/2304.01243/) (2023)
- [CD-TVD: Contrastive Diffusion for 3D Super-Resolution with Scarce High-Resolution Time-Varying Data](https://mlsanity.com/paper/2508.08173/) (2025)
- [Contrastive Learning for Low-light Raw Denoising](https://mlsanity.com/paper/2305.03352/) (2023)
- [Joint End-to-End Image Compression and Denoising: Leveraging Contrastive Learning and Multi-Scale Self-ONNs](https://mlsanity.com/paper/2402.05582/) (2024)
- [Harnessing Joint Rain-/Detail-aware Representations to Eliminate Intricate Rains](https://mlsanity.com/paper/2404.12091/) (2024)
- [Image Harmonization with Region-wise Contrastive Learning](https://mlsanity.com/paper/2205.14058/) (2022)
- [Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution](https://mlsanity.com/paper/2211.15951/) (2022)
- [DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution](https://mlsanity.com/paper/2311.18508/) (2023)
- [IRConStyle: Image Restoration Framework Using Contrastive Learning and Style Transfer](https://mlsanity.com/paper/2402.15784/) (2024)
- [WeatherCycle: Unpaired Multi-Weather Restoration via Color Space Decoupled Cycle Learning](https://mlsanity.com/paper/2509.23150/) (2025)
- [DualTSR: Unified Dual-Diffusion Transformer for Scene Text Image Super-Resolution](https://mlsanity.com/paper/2603.14207/) (2026)
- [A Hybrid CNN-Transformer Architecture with Frequency Domain Contrastive Learning for Image Deraining](https://mlsanity.com/paper/2308.03340/) (2023)
- [Unpaired MRI Super Resolution with Contrastive Learning](https://mlsanity.com/paper/2310.15767/) (2023)
- [Exploiting Self-Supervised Constraints in Image Super-Resolution](https://mlsanity.com/paper/2404.00260/) (2024)
- [Dynamic Resolution Guidance for Facial Expression Recognition](https://mlsanity.com/paper/2404.06365/) (2024)
- [MaCo-GAN: Manifold-Contrastive Adversarial Learning for Single Image Super-Resolution](https://mlsanity.com/paper/2606.05068/) (2026)

## Abstract (arXiv)

Contrastive learning has achieved remarkable success on various high-level tasks, but there are fewer contrastive learning-based methods proposed for low-level tasks. It is challenging to adopt vanilla contrastive learning technologies proposed for high-level visual tasks to low-level image restoration problems straightly. Because the acquired high-level global visual representations are insufficient for low-level tasks requiring rich texture and context information. In this paper, we investigate the contrastive learning-based single image super-resolution from two perspectives: positive and negative sample construction and feature embedding. The existing methods take naive sample construction approaches (e.g., considering the low-quality input as a negative sample and the ground truth as a positive sample) and adopt a prior model (e.g., pre-trained VGG model) to obtain the feature embedding. To this end, we propose a practical contrastive learning framework for SISR, named PCL-SR. We involve the generation of many informative positive and hard negative samples in frequency space. Instead of utilizing an additional pre-trained network, we design a simple but effective embedding network inherited from the discriminator network which is more task-friendly. Compared with existing benchmark methods, we re-train them by our proposed PCL-SR framework and achieve superior performance. Extensive experiments have been conducted to show the effectiveness and technical contributions of our proposed PCL-SR thorough ablation studies. The code and pre-trained models can be found at https://github.com/Aitical/PCL-SISR.