# UniDemoir'e: Towards Universal Image Demoir'eing with Data Generation and Synthesis

arXiv: https://arxiv.org/abs/2502.06324 · HTML: https://mlsanity.com/paper/2502.06324/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 54 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [Vector-quantized Image Modeling with Improved VQGAN](https://mlsanity.com/paper/2110.04627/) (2021)
- [Denoising Diffusion Models for Plug-and-Play Image Restoration](https://mlsanity.com/paper/2305.08995/) (2023)
- [Generating Images with Perceptual Similarity Metrics based on Deep Networks](https://mlsanity.com/paper/1602.02644/) (2016)
- [Generative Diffusion Prior for Unified Image Restoration and Enhancement](https://mlsanity.com/paper/2304.01247/) (2023)
- [Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method](https://mlsanity.com/paper/2212.11548/) (2022)
- [CARAFE: Content-Aware ReAssembly of FEatures](https://mlsanity.com/paper/1905.02188/) (2019)
- [Iterative Filter Adaptive Network for Single Image Defocus Deblurring](https://mlsanity.com/paper/2108.13610/) (2021)
- [Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models](https://mlsanity.com/paper/2304.08291/) (2023)
- [MixStyle Neural Networks for Domain Generalization and Adaptation](https://mlsanity.com/paper/2107.02053/) (2021)
- [Wavelet-Based Dual-Branch Network for Image Demoireing](https://mlsanity.com/paper/2007.07173/) (2020)
- [Image Demoireing with Learnable Bandpass Filters](https://mlsanity.com/paper/2004.00406/) (2020)
- [Convolutional Color Constancy](https://mlsanity.com/paper/1507.00410/) (2015)
- [All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation](https://mlsanity.com/paper/2308.03021/) (2023)
- [UHD-IQA Benchmark Database: Pushing the Boundaries of Blind Photo Quality Assessment](https://mlsanity.com/paper/2406.17472/) (2024)
- [AIM 2019 Challenge on Image Demoireing: Methods and Results](https://mlsanity.com/paper/1911.03461/) (2019)
- [Learning Image Demoireing from Unpaired Real Data](https://mlsanity.com/paper/2401.02719/) (2024)

## Built on by (most cited first, 6 of 6)

- [RealRestorer: Towards Generalizable Real-World Image Restoration with Large-Scale Image Editing Models](https://mlsanity.com/paper/2603.25502/) (2026)
- [Moir'e Zero: An Efficient and High-Performance Neural Architecture for Moir'e Removal](https://mlsanity.com/paper/2507.22407/) (2025)
- [SIDME: Self-supervised Image Demoir'eing via Masked Encoder-Decoder Reconstruction](https://mlsanity.com/paper/2504.12245/) (2025)
- [Fabric Image Demoir'eing Benchmark from Synthesis to Restoration](https://mlsanity.com/paper/2606.24072/) (2026)
- [Improving Complex Moir'e Removal with Generative Supervision](https://mlsanity.com/paper/2608.17883/) (2026)
- [DocIntent: Answerability-Guided Agentic Restoration for Real-World Document Visual Question Answering](https://mlsanity.com/paper/2608.29037/) (2026)

## Abstract (arXiv)

Image demoir'eing poses one of the most formidable challenges in image restoration, primarily due to the unpredictable and anisotropic nature of moir'e patterns. Limited by the quantity and diversity of training data, current methods tend to overfit to a single moir'e domain, resulting in performance degradation for new domains and restricting their robustness in real-world applications. In this paper, we propose a universal image demoir'eing solution, UniDemoir'e, which has superior generalization capability. Notably, we propose innovative and effective data generation and synthesis methods that can automatically provide vast high-quality moir'e images to train a universal demoir'eing model. Our extensive experiments demonstrate the cutting-edge performance and broad potential of our approach for generalized image demoir'eing.