Color Filter Array Demosaicking Using Densely Connected Residual Network Article Swipe
Bumjun Park
,
Jechang Jeong
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/access.2019.2939578
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/access.2019.2939578
Deep convolutional neural networks have been used extensively in recent image processing research, exhibiting drastically improved performance. In this study, we apply convolutional neural networks to color filter array demosaicking, which plays an essential role in single-sensor digital cameras. Contrary to conventional convolutional neural network-based demosaicking models, the proposed model does not require any initial interpolation step for mosaicked input images, which increases the computational complexity. Using a mosaicked image as input, the proposed model is trained in an end-to-end manner to generate demosaicked images outputs. Many deep neural networks experience vanishing-gradient problem, which makes models hard to be trained. To solve this problem, we apply residual learning and densely connected convolutional neural network. Moreover, we apply block-wise convolutional neural networks to consider local features. Finally, we apply a sub-pixel interpolation layer to generate demosaicked output images more efficiently and accurately. Experimental results show that our proposed model outperforms conventional solutions and state-of-the-art models.
Related Topics
Concepts
Demosaicing
Convolutional neural network
Computer science
Artificial intelligence
Residual
Interpolation (computer graphics)
Artificial neural network
Pixel
Block (permutation group theory)
Color filter array
Pattern recognition (psychology)
Filter (signal processing)
Computer vision
Deep learning
Image (mathematics)
Image processing
Algorithm
Layer (electronics)
Color gel
Color image
Mathematics
Geometry
Organic chemistry
Chemistry
Thin-film transistor
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2019.2939578
- https://ieeexplore.ieee.org/ielx7/6287639/8600701/08825809.pdf
- OA Status
- gold
- Cited By
- 16
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2971374671
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2971374671Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2019.2939578Digital Object Identifier
- Title
-
Color Filter Array Demosaicking Using Densely Connected Residual NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-01-01Full publication date if available
- Authors
-
Bumjun Park, Jechang JeongList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2019.2939578Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8600701/08825809.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8600701/08825809.pdfDirect OA link when available
- Concepts
-
Demosaicing, Convolutional neural network, Computer science, Artificial intelligence, Residual, Interpolation (computer graphics), Artificial neural network, Pixel, Block (permutation group theory), Color filter array, Pattern recognition (psychology), Filter (signal processing), Computer vision, Deep learning, Image (mathematics), Image processing, Algorithm, Layer (electronics), Color gel, Color image, Mathematics, Geometry, Organic chemistry, Chemistry, Thin-film transistorTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
16Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 2, 2023: 2, 2022: 5, 2020: 3Per-year citation counts (last 5 years)
- References (count)
-
39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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