# Moir'e Photo Restoration Using Multiresolution Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1805.02996 · HTML: https://mlsanity.com/paper/1805.02996/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2018.

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

- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [TAPE: Task-Agnostic Prior Embedding for Image Restoration](https://mlsanity.com/paper/2203.06074/) (2022)
- [WaveDM: Wavelet-Based Diffusion Models for Image Restoration](https://mlsanity.com/paper/2305.13819/) (2023)
- [Reduce Information Loss in Transformers for Pluralistic Image Inpainting](https://mlsanity.com/paper/2205.05076/) (2022)
- [Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoireing](https://mlsanity.com/paper/2207.09935/) (2022)
- [Video Demoireing with Relation-Based Temporal Consistency](https://mlsanity.com/paper/2204.02957/) (2022)
- [Seeing the Unseen: A Frequency Prompt Guided Transformer for Image Restoration](https://mlsanity.com/paper/2404.00288/) (2024)
- [Image Demoireing in RAW and sRGB Domains](https://mlsanity.com/paper/2312.09063/) (2023)
- [UniRestorer: Universal Image Restoration via Adaptively Estimating Image Degradation at Proper Granularity](https://mlsanity.com/paper/2412.20157/) (2024)
- [Real-Time Image Demoireing on Mobile Devices](https://mlsanity.com/paper/2302.02184/) (2023)
- [Recaptured Raw Screen Image and Video Demoir'eing via Channel and Spatial Modulations](https://mlsanity.com/paper/2310.20332/) (2023)
- [FC3DNet: A Fully Connected Encoder-Decoder for Efficient Demoir'eing](https://mlsanity.com/paper/2406.14912/) (2024)
- [Direction-aware Video Demoireing with Temporal-guided Bilateral Learning](https://mlsanity.com/paper/2308.13388/) (2023)
- [SiamTrans: Zero-Shot Multi-Frame Image Restoration with Pre-Trained Siamese Transformers](https://mlsanity.com/paper/2112.09426/) (2021)
- [FPANet: Frequency-based Video Demoireing using Frame-level Post Alignment](https://mlsanity.com/paper/2301.07330/) (2023)
- [Moir'e Attack (MA): A New Potential Risk of Screen Photos](https://mlsanity.com/paper/2110.10444/) (2021)
- [DSDNet: Raw Domain Demoir'eing via Dual Color-Space Synergy](https://mlsanity.com/paper/2504.15756/) (2025)
- [DGIQA: Depth-guided Feature Attention and Refinement for Generalizable Image Quality Assessment](https://mlsanity.com/paper/2505.24002/) (2025)
- [Video Demoireing using Focused-Defocused Dual-Camera System](https://mlsanity.com/paper/2508.03449/) (2025)
- [Fast and Light-Weight Network for Single Frame Structured Illumination Microscopy Super-Resolution](https://mlsanity.com/paper/2111.09103/) (2021)
- [Docmarking: Real-Time Screen-Cam Robust Document Image Watermarking](https://mlsanity.com/paper/2304.12682/) (2023)
- [MoCHA-former: Moir'e-Conditioned Hybrid Adaptive Transformer for Video Demoir'eing](https://mlsanity.com/paper/2508.14423/) (2025)
- [Through the Lens: Benchmarking Deepfake Detectors Against Moir'e-Induced Distortions](https://mlsanity.com/paper/2510.23225/) (2025)
- [AADNet: Attention aware Demoir'eing Network](https://mlsanity.com/paper/2403.08384/) (2024)
- [Look-Around Before You Leap: High-Frequency Injected Transformer for Image Restoration](https://mlsanity.com/paper/2404.00279/) (2024)
- [ShapeMoir'e: Channel-Wise Shape-Guided Network for Image Demoir'eing](https://mlsanity.com/paper/2404.18155/) (2024)
- [SIDME: Self-supervised Image Demoir'eing via Masked Encoder-Decoder Reconstruction](https://mlsanity.com/paper/2504.12245/) (2025)
- [Freqformer: Image-Demoir'eing Transformer via Effective Frequency Decomposition](https://mlsanity.com/paper/2505.19120/) (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)

## Abstract (arXiv)

Digital cameras and mobile phones enable us to conveniently record precious moments. While digital image quality is constantly being improved, taking high-quality photos of digital screens still remains challenging because the photos are often contaminated with moir'e patterns, a result of the interference between the pixel grids of the camera sensor and the device screen. Moir'e patterns can severely damage the visual quality of photos. However, few studies have aimed to solve this problem. In this paper, we introduce a novel multiresolution fully convolutional network for automatically removing moir'e patterns from photos. Since a moir'e pattern spans over a wide range of frequencies, our proposed network performs a nonlinear multiresolution analysis of the input image before computing how to cancel moir'e artefacts within every frequency band. We also create a large-scale benchmark dataset with 100,000^+ image pairs for investigating and evaluating moir'e pattern removal algorithms. Our network achieves state-of-the-art performance on this dataset in comparison to existing learning architectures for image restoration problems.