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Cited by 7Advanced Image Processing TechniquesImage Enhancement TechniquesImage and Signal Denoising Methods

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Multibranch Wavelet-Based Network for Image Demoiréing

Open access
Yeh, Chia-Hung, Lo, Chen, He, Cheng-Han·2024·Sensors
DOI 10.3390/s24092762PMC PMC11086364
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Abstract

Moiré patterns caused by aliasing between the camera's sensor and the monitor can severely degrade image quality. Image demoiréing is a multi-task image restoration method that includes texture and color restoration. This paper proposes a new multibranch wavelet-based image demoiréing network (MBWDN) for moiré pattern removal. Moiré images are separated into sub-band images using wavelet decomposition, and demoiréing can be achieved using the different learning strategies of two networks: moiré removal network (MRN) and detail-enhanced moiré removal network (DMRN). MRN removes moiré patterns from low-frequency images while preserving the structure of smooth areas. DMRN simultaneously removes high-frequency moiré patterns and enhances fine details in images. Wavelet decomposition is used to replace traditional upsampling, and max pooling effectively increases the receptive field of the network without losing the spatial information. Through decomposing the moiré image into different levels using wavelet transform, the feature learning results of each branch can be fully preserved and fed into the next branch; therefore, possible distortions in the recovered image are avoided. Thanks to the separation of high- and low-frequency images during feature training, the proposed two networks achieve impressive moiré removal effects. Based on extensive experiments conducted using public datasets, the proposed method shows good demoiréing validity both quantitatively and qualitatively when compared with the state-of-the-art approaches.

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Figures

Figure 1 Moiré textures of different scales, frequencies, and colors.
Figure 1 Moiré textures of different scales, frequencies, and colors.CC BY · PubMed Central
Figure 2 Illustration of the network architecture of our proposed method.
Figure 2 Illustration of the network architecture of our proposed method.CC BY · PubMed Central
Figure 3 Illustration of the network architecture of the proposed MRN and DMRN.
Figure 3 Illustration of the network architecture of the proposed MRN and DMRN.CC BY · PubMed Central
Figure 4 Comparisons of visual quality among DMCNN, WDNet, MBCNN, and UHDM.
Figure 4 Comparisons of visual quality among DMCNN, WDNet, MBCNN, and UHDM.CC BY · PubMed Central
Figure 5 Comparisons of visual quality evaluation of complex patterns areas among DMCNN, WDNet, MBCNN, and UHDM.
Figure 5 Comparisons of visual quality evaluation of complex patterns areas among DMCNN, WDNet, MBCNN, and UHDM.CC BY · PubMed Central
Figure 6 Real moiré images and the corresponding outputs of our proposed method.
Figure 6 Real moiré images and the corresponding outputs of our proposed method.CC BY · PubMed Central

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Citations

Cites

Show all 6 references

Research works printed but not linked: 41

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Cited by

Sorted by citations

Metadata

Authors
Yeh, C.-H., Lo, C., & He, C.-H.
Affiliations
National Taiwan Normal University (TW)
Corresponding author
Yeh, Chia-Hung
ORCID
Yeh, Chia-Hung
Venue
Sensors
Publisher
MDPI AG
Published
2024-04-26
Volume
24
Issue
9
Pages
2762
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s24092762
PMC
PMC11086364
OpenAlex
W4395684992
Cited by
7
References
6
Citations by year
2026: 3 · 2025: 3 · 2024: 1
FWCI
0.98
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Ministry of Science and Technology, Taiwan (MOST 110-2221-E-003-005-MY3, MOST 111-2221-E-003-007-MY3)
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
wavelet decomposition, texture restoration, color restoration, detail enhancement, max pooling, image upscaling

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