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Cited by 50Advanced Image Processing TechniquesImage Enhancement TechniquesImage and Signal Denoising Methods

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Multi-Scale Dynamic Feature Encoding Network for Image Demoiréing

Open access
Cheng, Xi, Fu, Zhenyong, Yang, Jian·2019·2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
DOI 10.1109/iccvw.2019.00432
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Abstract

The prevalence of digital sensors, such as digital cameras and mobile phones, simplifies the acquisition of photos. Digital sensors, however, suffer from producing Moire when photographing objects having complex textures, which deteriorates the quality of photos. Moire spreads across various frequency bands of images and is a dynamic texture with varying colors and shapes, which pose two main challenges in demoireing - an important task in image restoration. In this paper, towards addressing the first challenge, we design a multi-scale network to process images at different spatial resolutions, obtaining features in different frequency bands, and thus our method can jointly remove moire in different frequency bands. Towards solving the second challenge, we propose a dynamic feature encoding module (DFE), embedded in each scale, for dynamic texture. Moire pattern can be eliminated more effectively via DFE.Our proposed method, termed Multi-scale convolutional network with Dynamic feature encoding for image DeMoireing (MDDM), can outperform the state of the arts in fidelity as well as perceptual on benchmarks.

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Crossref · 10.1109/iccvw.2019.00432

Cites

4 of 4 references verified0 citation errors · 0 not checkable

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  • Recent multi-scale and U-Net-based demoiréing methods still fail to fully capture the global characteristics of moiré artifacts.

    “While recent methods adopt multi-scale architectures [7, 1, 8, 14] and symmetric encoder-decoder networks such as U-Net [24, 6, 2], they still fail to fully capture the global characteristics of moir´ e artifacts.”

    From MoCHA-former: Moiré-Conditioned Hybrid Adaptive Transformer for Video Demoiréing (2026) · cites this paper as [8]

Citations

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Flagged first, then by citations

Research works printed but not linked: 20

  1. 10.1109/CVPR.2017.618 · DOI
  2. lin, 2013, Network in Network
  3. liu, 2018, Demoir\‘eing of camera-captured screen images using deep convolutional neural network
  4. liu, 0, Moiré pattern removal from texture images via low-rank and sparse matrix decomposition, 2015 Visual Communications and Image Processing (VCIP), 1
  5. 10.1109/CVPR.2016.207 · DOI · Paper page
  6. wang, 2017, Zm-net Real-time zero-shot image manipulation network
  7. 10.1016/j.micron.2011.07.009 · DOI
  8. yang, 0, Demoiréing for screen-shot images with multi-channel layer decomposition, 2017 IEEE Visual Communications and Image Processing (VCIP), 1
  9. 10.1109/TPAMI.2015.2439281 · DOI · Paper page
  10. 10.1109/ICCV.2015.123 · DOI · Paper page
  11. gao, 0, Moiré pattern removal with multi-scale feature enhancing network, 2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 240
  12. 10.1109/CVPR.2016.182 · DOI · Paper page
  13. 10.1109/ICCV.2017.167 · DOI · Paper page
  14. 10.1109/CVPR.2018.00854 · DOI
  15. 10.3390/app9152992 · DOI
  16. yuan, 0, Aim 2019 challenge on image demoreing: dataset and study, The IEEE International Conference on Computer Vision (ICCV) Workshops
  17. matthew, 0, Deconvolutional networks, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2528
  18. yuan, 0, Aim 2019 challenge on image demoreing: methods and results, The IEEE International Conference on Computer Vision (ICCV) Workshops
  19. 10.1007/978-3-030-01249-6_17 · DOI
  20. 10.1109/TIP.2017.2662206 · DOI · Paper page

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Metadata

Authors
Cheng, X., Fu, Z., & Yang, J.
Affiliations
Nanjing University of Science and Technology (CN)
ORCID
Venue
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Publisher
IEEE
Published
2019-10
Pages
3486-3493
ISSN
2473-9936 (ISSN-L)
DOI
10.1109/iccvw.2019.00432
OpenAlex
W2975325359
Cited by
50
References
4
Citations by year
2026: 7 · 2025: 10 · 2024: 7 · 2023: 8 · 2022: 9 · 2021: 5 · 2020: 4
FWCI
1.47
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE International Conference on Computer Vision Workshops (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
Keywords
multi-scale convolutional neural network, dynamic textures, image restoration

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