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Cited by 9Advanced Image Processing TechniquesImage Enhancement TechniquesImage and Signal Denoising Methods

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

C3net: demoiréing network attentive in channel, color, and concatenation

Kim, Sangmin, Nam, Hyungjoon, Kim, Jisu, Jeong, Jechang·2020·2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
DOI 10.1109/cvprw50498.2020.00221
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Abstract

Attentive neural networks for image restoration are in the spotlight because they got remarkable results both qualitatively and quantitatively. Networks attentive in RGB channels were effective in fields such as single image super-resolution and RAW to RGB mapping. In addition, networks attentive in positions of pixels were used in image denoising. However, networks attentive in positions of pixels, so called spatial attention or pixel attention algorithm, were not as effective in image restoration because the number of pixels in patches of an image is so many that the weights by sigmoid function are insignificant. Also, networks attentive in positions of pixels were mainly used in high-level vision such as image classification and image captioning where there is no need to restore an image itself. In this paper, we propose a demoiréing network attentive in channel, color, and concatenation, named C3Net. The proposed algorithm uses residual blocks attentive in RGB channels to take advantage of channel attention algorithm. In addition, we introduce a L1 color loss for demoiréing to solve moiré patterns caused by color-striped patterns. Also, we transferred multi-scale information by concatenation, not multiplying with the insignificant weights by sigmoid function. As a result, our proposed C3Net showed state-of-the-art results in the benchmark dataset on NTIRE 2020 demoiréing challenge.

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Metadata

Authors
Kim, S., Nam, H., Kim, J., & Jeong, J.
Affiliations
Hanyang University (KR)
ORCID
Venue
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Publisher
IEEE
Published
2020-06
Pages
1713-1721
DOI
10.1109/cvprw50498.2020.00221
OpenAlex
W3035157648
Cited by
9
References
6
Citations by year
2026: 1 · 2025: 3 · 2024: 2 · 2022: 3
FWCI
0.23
Type
conference paper
Language
en
Open access
closed
Licence
Locations
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
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
channel attention, color attention, image restoration, multi-scale feature fusion

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CoChat. (2026). Citation check: C3net: demoiréing network attentive in channel, color, and concatenation. https://app.cochat.ai/papers/10.1109/cvprw50498.2020.00221
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Kim, S., Nam, H., Kim, J., & Jeong, J. (2020). C3net: demoiréing network attentive in channel, color, and concatenation. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/cvprw50498.2020.00221