├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 Shuning Xu 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Paper List| Image Demoireing & Video Demoireing 2 | 3 | This repository compiles a list of papers related to **Image/ Video Demoireing**. (Updating) 4 | 5 | Continual improvements are being made to this repository. If you come across any relevant papers that should be included, please don't hesitate to open an issue. 6 | 7 | ## Contents 8 | 9 | - [Papers](#demoireing-papers) 10 | - [Image Demoiring Papers](#image-demoireing-papers) 11 | - [Video Demoiring Papers](#video-demoireing-papers) 12 | - [Challenges](#demoireing-challenges) 13 | - [Datasets](#demoireing-datasets) 14 | - [Image Demoireing Datasets](#image-demoireing-datasets) 15 | - [Video Demoireing Datasets](#video-demoireing-datasets) 16 | 17 | ## Demoireing Papers 18 | 19 | ### Image Demoireing Papers 20 | 21 | | Title | Paper | Code | Dataset | Key Words | 22 | | --- | --- | --- | --- | --- | 23 | | Video Demoireing using Focused-Defocused Dual-Camera System | [TPAMI-2025](https://www.arxiv.org/abs/2508.03449) | [dual_lens-demoireing](https://github.com/circle11111/dual_lens_demoireing) | | Focused-Defocused | 24 | | Image DeMoiréing Using Dual Camera Fusion on Mobile Phones | [ICME-2025](https://arxiv.org/pdf/2506.08361) | [DCID](https://github.com/Mrduckk/DCID) | [DCID](https://github.com/Mrduckk/DCID) | Dual Camera | 25 | | Freqformer: Image-Demoir\'eing Transformer via Efficient Frequency Decomposition | [arxiv-2025](https://arxiv.org/pdf/2505.19120) | [Freqformer](https://github.com/xyLiu339/Freqformer) | | | 26 | | DSDNet: Raw Domain Demoiréing via Dual Color-Space Synergy | [ICML-2025](https://arxiv.org/abs/2504.15756) | | | | 27 | | Pyramid Learnable Bandpass Filters for Ultra-High-Definition Image Demoiréing | [TCSVT-2025](https://ieeexplore.ieee.org/abstract/document/10904420) | | | | 28 | | P-BiC: Ultra-High-Definition Image Moiré Patterns Removal via Patch Bilateral Compensation | [ACMMM-2024](https://openreview.net/pdf?id=DntswNJ3RN) | | | | 29 | | Image Demoiréing in RAW and sRGB Domains | [ECCV-2024](https://arxiv.org/pdf/2312.09063.pdf) | [RRID](https://github.com/rebeccaeexu/RRID) | [TMM22](https://github.com/tju-chengyijia/RDNet) | | 30 | | Multibranch Wavelet-Based Network for Image Demoiréing | [Sensors-2024](https://www.mdpi.com/1424-8220/24/9/2762) | | | | 31 | | Image Demoiréing via Multi-scale Fusion Networks with Moiré Data Augmentation | [Sensors-2024](https://ieeexplore.ieee.org/abstract/document/10510240) | | | | 32 | | Learning Image Demoiréing from Unpaired Real Data | [AAAI-2024](https://arxiv.org/pdf/2401.02719) | [UnDem](https://github.com/zysxmu/UnDeM) | | | 33 | | Coarse-to-fine Disentangling Demoiréing Framework for Recaptured Screen Images | [TPAMI-2023](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10040914) | | | | 34 | | Doing More With Moiré Pattern Detection in Digital Photos | [TIP-2023](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10006755) | [MoireDet](https://github.com/cong-yang/MoireDet) | | | 35 | | Progressive Moire Removal and Texture Complementation for Image Demoireing | [TCSVT-2023](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10019292) | / | | | 36 | | Adaptive Multispectral Encoding Network for Image Demoiréing | [TIM-2023](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10138103) | | | | 37 | | Real-Time Image Demoireing on Mobile Devices | [ICLR-2023](https://arxiv.org/pdf/2302.02184.pdf) | [DDA](https://github.com/zyxxmu/DDA) | | | 38 | | Recaptured Screen Image Demoireing in Raw Domain | [TMM-2022](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9855881) | [RDNet](https://github.com/tju-chengyijia/RDNet) | [TMM22](https://github.com/tju-chengyijia/RDNet) | | 39 | | Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoiréing | [ECCV-2022](https://arxiv.org/abs/2207.09935) | [ESDNet](https://github.com/CVMI-Lab/UHDM) | [UHDM](https://drive.google.com/drive/folders/1DyA84UqM7zf3CeoEBNmTi_dJ649x2e7e) | UHDM | 40 | | Unsupervised Moiré Pattern Removal for Recaptured Screen Images | [Neurocomputing-2021](https://www.sciencedirect.com/science/article/pii/S0925231221008626) | [MRGAN](https://github.com/JerryLeolfl/pytorch-MRGAN-master) | | | 41 | | Morié Attack (MA): A New Potential Risk of Screen Photos | [NeurIPS-2021](https://proceedings.neurips.cc/paper/2021/hash/db9eeb7e678863649bce209842e0d164-Abstract.html) | [MA](https://github.com/Dantong88/Moire_Attack) | | | 42 | | Image Demoiréing with a Dual-Domain Distilling Network | [ICME-2021](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9428091) | | | | 43 | | Learning Frequency Domain Priors for Image Demoireing | [TPAMI-2021](https://ieeexplore.ieee.org/abstract/document/9547736) | [MBCNN](https://github.com/zhenngbolun/Learnbale_Bandpass_Filter) | | | 44 | | Self-Adaptively Learning to Demoiré from Focused and Defocused Image Pairs | [NeurIPS-2020](https://proceedings.neurips.cc/paper/2020/hash/fd348179ec677c5560d4cd9c3ffb6cd9-Abstract.html) | | | | 45 | | Recaptured screen image demoiréing | [TCSVT-2020](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8972378) | [AMNet](https://github.com/tju-maoyan/AMNet) | | | 46 | | Deep Wavelet Network With Domain Adaptation for Single Image Demoireing | [CVPRW-2020](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w31/Luo_Deep_Wavelet_Network_With_Domain_Adaptation_for_Single_Image_Demoireing_CVPRW_2020_paper.pdf) | / | | | 47 | | MMDM: Multi-frame and multi-scale for image demoiréing | [CVPRW-2020](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w31/Liu_MMDM_Multi-Frame_and_Multi-Scale_for_Image_Demoireing_CVPRW_2020_paper.pdf) | [MMDM](https://github.com/q935970314/MMDM) | | | 48 | | Moire Pattern Removal via Attentive Fractal Network | [CVPRW-2020](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w31/Xu_Moire_Pattern_Removal_via_Attentive_Fractal_Network_CVPRW_2020_paper.pdf) | [AFN](https://github.com/Ir1d/AFN) | | | 49 | | C3Net: Demoireing Network Attentive in Channel, Color and Concatenation | [CVPRW-2020](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w31/Kim_C3Net_Demoireing_Network_Attentive_in_Channel_Color_and_Concatenation_CVPRW_2020_paper.pdf) | [C3Net](https://github.com/bmycheez/C3Net) | | | 50 | | Dual-domain deep convolutional neural networks for image demoireing | [CVPRW-2020](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w31/Vien_Dual-Domain_Deep_Convolutional_Neural_Networks_for_Image_Demoireing_CVPRW_2020_paper.pdf) | / | | | 51 | | Image Demoireing with Learnable Bandpass Filters | [CVPR-2020](https://arxiv.org/abs/2004.00406) | [MBCNN](https://github.com/zhenngbolun/Learnbale_Bandpass_Filter) | [LCDMoiré](https://arxiv.org/abs/1911.03461), [TIP18](https://yujingsun.github.io/dataset/moireDatareadMe.txt) | | 52 | | FHDe2Net: Full High Definition Demoireing Network | [ECCV-2020](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123670715.pdf) | [FHDe2Net](https://github.com/PKU-IMRE/FHDe2Net) | [FHDMi](https://github.com/PKU-IMRE/FHDe2Net) | | 53 | | Wavelet-Based Dual-Branch Network for Image Demoireing | [ECCV-2020](https://arxiv.org/abs/2007.07173) | [WDnet](https://github.com/laulampaul/WDNet_demoire) | | | 54 | | Multi-Scale Dynamic Feature Encoding Network for Image Demoiréing | [ICCVW-2019](https://ieeexplore.ieee.org/document/9022550) | [MDDM](https://github.com/opteroncx/MDDM) | | | 55 | | Mop Moire Patterns Using MopNet | [ICCV-2019](https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Mop_Moire_Patterns_Using_MopNet_ICCV_2019_paper.pdf) | [MopNet](https://github.com/PKU-IMRE/MopNet) | [TIP18](https://yujingsun.github.io/dataset/moireDatareadMe.txt) | Multi-scale + 2 sub-modules | 56 | | Moiré Photo Restoration Using Multiresolution Convolutional Neural Networks | [TIP-2018](https://arxiv.org/abs/1805.02996) | [DMCNN-unofficial code](https://github.com/ZhengJun-AI/MoirePhotoRestoration-MCNN) | [TIP18](https://yujingsun.github.io/dataset/moireDatareadMe.txt) | Multi-resolution CNN | 57 | | Demoireing of Camera-Captured Screen Images Using Deep Convolutional Neural Network | [arxiv-2018](https://arxiv.org/pdf/1804.03809.pdf) | / | | | 58 | 59 | ### Video Demoireing Papers 60 | 61 | | Title | Paper | Code | Dataset | Key Words | 62 | | --- | --- | --- | --- | --- | 63 | | STD-Net: Spatio-Temporal Decomposition Network for Video Demoiréing with Sparse Transformers | [TCSVT-2024](https://ieeexplore.ieee.org/abstract/document/10495363) | | | | 64 | | Video Demoiréing with Deep Temporal Color Embedding and Video-Image Invertible Consistency | [TMM-2024](https://ieeexplore.ieee.org/abstract/document/10445438) | | [VDmoire](https://daipengwa.github.io/VDmoire_ProjectPage/) | | 65 | | Direction-aware Video Demoireing with Temporal-guided Bilateral Learning | [AAAI-2024](https://arxiv.org/abs/2308.13388) | [DTNet](https://github.com/rebeccaeexu/DTNet) | [VDmoire](https://daipengwa.github.io/VDmoire_ProjectPage/) | | 66 | | Recaptured Raw Screen Image and Video Demoiréing via Channel and Spatial Modulations | [NeurIPS-2023](https://arxiv.org/pdf/2310.20332.pdf) | [VD_raw](https://github.com/tju-chengyijia/VD_raw) | [RawVDemoiré](https://github.com/tju-chengyijia/VD_raw) | Raw Video Demoiréing | 67 | | Deep Video Demoireing via Compact Invertible Dyadic Decomposition | [ICCV-2023](https://openaccess.thecvf.com/content/ICCV2023/papers/Quan_Deep_Video_Demoireing_via_Compact_Invertible_Dyadic_Decomposition_ICCV_2023_paper.pdf) | [CIDNet](https://github.com/RuotaoXu/CIDNet) | | | 68 | | FPANet: Frequency-based Video Demoireing using Frame-level Post Alignment | [arxiv-2023](https://arxiv.org/abs/2301.07330) | / | [VDmoire](https://daipengwa.github.io/VDmoire_ProjectPage/) | | 69 | | Video Demoiréing with Relation-Based Temporal Consistency | [CVPR-2022](https://openaccess.thecvf.com/content/CVPR2022/papers/Dai_Video_Demoireing_With_Relation-Based_Temporal_Consistency_CVPR_2022_paper.pdf) | [VDmoire](https://github.com/CVMI-Lab/VideoDemoireing) | [VDmoire](https://daipengwa.github.io/VDmoire_ProjectPage/) | Video Demoiréing | 70 | 71 | ## Demoireing Challenges 72 | 73 | | Title | Dataset | Key Words | 74 | | --- | --- | --- | 75 | | [NTIRE 2020 Challenge on Image Demoireing: Methods and Results](https://arxiv.org/abs/2005.03155) | CFAMoire | | 76 | | [AIM 2019 Challenge on Image Demoireing: Methods and Results](https://arxiv.org/abs/1911.03461) | LCDMoire | First Image Demoireing Challenge | 77 | 78 | ## Demoireing Datasets 79 | 80 | ### Image Demoireing Datasets 81 | 82 | | Dataset | Paper | Avg. Resolution | Size | Diversity | Real World | 83 | | --- | --- | --- | --- | --- | --- | 84 | | [TIP-2018](https://arxiv.org/abs/1805.02996) | [TIP-2018](https://arxiv.org/abs/1805.02996) | 256 × 256 | 135,000 | No text scenes | ✔ | 85 | | [LCDMoiré](https://arxiv.org/abs/1911.03461) | [CVPR-2020](https://arxiv.org/abs/2004.00406) | 1024 × 1024 | 10,200 | Only text scenes | ✖ | 86 | | [FHDMi](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123670715.pdf) | [ECCV-2020](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123670715.pdf) | 1920 × 1080 | 12,000 | Diverse scenes | ✔ | 87 | | [UHDM](https://drive.google.com/drive/folders/1DyA84UqM7zf3CeoEBNmTi_dJ649x2e7e) | [ECCV-2022](https://arxiv.org/abs/2207.09935) | 4328 × 3248 | 5,000 | Diverse scenes | ✔ | 88 | | [TMM22](https://github.com/tju-chengyijia/RDNet) | [TMM-2022](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9855881) | (Training) 256 × 256 / (Testing) 512× 512 | 63,588 | Diverse scenes | ✔ | 89 | 90 | ### Video Demoireing Datasets 91 | 92 | | Dataset | Paper | Resolution | Size | Note | 93 | | --- | --- | --- | --- | --- | 94 | | [VDmoire](https://daipengwa.github.io/VDmoire_ProjectPage/) | [CVPR-2022](https://openaccess.thecvf.com/content/CVPR2022/papers/Dai_Video_Demoireing_With_Relation-Based_Temporal_Consistency_CVPR_2022_paper.pdf) | 720 p | (247 + 43) * 60 | sRGB | 95 | | [RawVDemoiré](https://github.com/tju-chengyijia/VD_raw) | [NeurIPS-2023](https://arxiv.org/pdf/2310.20332.pdf) | 720 p | (250 + 50) * 60 | Source:[sRGB, RAW]; Target: [sRGB] | 96 | --------------------------------------------------------------------------------