# Learning Invariant Representation for Unsupervised Image Restoration

arXiv: https://arxiv.org/abs/2003.12769 · HTML: https://mlsanity.com/paper/2003.12769/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2020.

## Built on by (most cited first, 28 of 28)

- [Generative Diffusion Prior for Unified Image Restoration and Enhancement](https://mlsanity.com/paper/2304.01247/) (2023)
- [Unsupervised Image Denoising in Real-World Scenarios via Self-Collaboration Parallel Generative Adversarial Branches](https://mlsanity.com/paper/2308.06776/) (2023)
- [Transfer CLIP for Generalizable Image Denoising](https://mlsanity.com/paper/2403.15132/) (2024)
- [A Survey on the Robustness of Computer Vision Models against Common Corruptions](https://mlsanity.com/paper/2305.06024/) (2023)
- [Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising](https://mlsanity.com/paper/2305.15887/) (2023)
- [Learning Distortion Invariant Representation for Image Restoration from A Causality Perspective](https://mlsanity.com/paper/2303.06859/) (2023)
- [Beyond Pixels: Text Enhances Generalization in Real-World Image Restoration](https://mlsanity.com/paper/2412.00878/) (2024)
- [Learning Degradation-Independent Representations for Camera ISP Pipelines](https://mlsanity.com/paper/2307.00761/) (2023)
- [Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior](https://mlsanity.com/paper/2112.01766/) (2021)
- [Turbo: Opportunistic Enhancement for Edge Video Analytics](https://mlsanity.com/paper/2207.00172/) (2022)
- [Multi-view Self-supervised Disentanglement for General Image Denoising](https://mlsanity.com/paper/2309.05049/) (2023)
- [Re-boosting Self-Collaboration Parallel Prompt GAN for Unsupervised Image Restoration](https://mlsanity.com/paper/2408.09241/) (2024)
- [Federated CycleGAN for Privacy-Preserving Image-to-Image Translation](https://mlsanity.com/paper/2106.09246/) (2021)
- [Step-Calibrated Diffusion for Biomedical Optical Image Restoration](https://mlsanity.com/paper/2403.13680/) (2024)
- [Unsupervised Image Denoising with Frequency Domain Knowledge](https://mlsanity.com/paper/2111.14362/) (2021)
- [Masked Pre-training Enables Universal Zero-shot Denoiser](https://mlsanity.com/paper/2401.14966/) (2024)
- [Modeling Dual-Exposure Quad-Bayer Patterns for Joint Denoising and Deblurring](https://mlsanity.com/paper/2412.07256/) (2024)
- [Learning Deblurring Texture Prior from Unpaired Data with Diffusion Model](https://mlsanity.com/paper/2507.13599/) (2025)
- [A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective](https://mlsanity.com/paper/2111.13078/) (2021)
- [Hierarchical Disentangled Representation for Invertible Image Denoising and Beyond](https://mlsanity.com/paper/2301.13358/) (2023)
- [LIR: A Lightweight Baseline for Image Restoration](https://mlsanity.com/paper/2402.01368/) (2024)
- [IRConStyle: Image Restoration Framework Using Contrastive Learning and Style Transfer](https://mlsanity.com/paper/2402.15784/) (2024)
- [UDC-VIT: A Real-World Video Dataset for Under-Display Cameras](https://mlsanity.com/paper/2501.18545/) (2025)
- [Learning Weighting Map for Bit-Depth Expansion within a Rational Range](https://mlsanity.com/paper/2204.12039/) (2022)
- [Generative Modeling in Structural-Hankel Domain for Color Image Inpainting](https://mlsanity.com/paper/2211.13857/) (2022)
- [Image Denoising via Style Disentanglement](https://mlsanity.com/paper/2309.14755/) (2023)
- [Diffusion Priors for Variational Likelihood Estimation and Image Denoising](https://mlsanity.com/paper/2410.17521/) (2024)
- [ESDiff: Encoding Strategy-inspired Diffusion Model with Few-shot Learning for Color Image Inpainting](https://mlsanity.com/paper/2504.17524/) (2025)

## Abstract (arXiv)

Recently, cross domain transfer has been applied for unsupervised image restoration tasks. However, directly applying existing frameworks would lead to domain-shift problems in translated images due to lack of effective supervision. Instead, we propose an unsupervised learning method that explicitly learns invariant presentation from noisy data and reconstructs clear observations. To do so, we introduce discrete disentangling representation and adversarial domain adaption into general domain transfer framework, aided by extra self-supervised modules including background and semantic consistency constraints, learning robust representation under dual domain constraints, such as feature and image domains. Experiments on synthetic and real noise removal tasks show the proposed method achieves comparable performance with other state-of-the-art supervised and unsupervised methods, while having faster and stable convergence than other domain adaption methods.