Quadratic Taylor Approximation Demosaicking System Using Post-Processing for Zipper Artifact Removal Article Swipe
Daejun Park
,
Jechang Jeong
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1109/access.2018.2874010
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1109/access.2018.2874010
In this paper, we propose a new demosaicking system that uses post-processing technique to remove zipper artifacts that are primarily caused by improper interpolation in a quincunx-sampled green plane. We use a high-order approximation to predict accurate pixel values in the weighted average-based green channel interpolation. We also perform prediction using a color difference model in the red and blue channel interpolation process. As a well-designed demosaicking system too cannot prevent the occurrence of ON-OFF pattern artifacts and the resulting false colors, we propose post-processing for zipper artifact detection and removal to improve the demosaicked image. After performing the just noticeable difference modelbased zipper artifact detection in the luminance image of the demosaicked image, the clamping-based zipper artifact removal process is performed in the red, green, and blue planes. Experimental results demonstrate that the proposed demosaicking system improves both the objective and subjective image qualities compared to the conventional state-of-the-art demosaicking algorithms.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2018.2874010
- OA Status
- gold
- Cited By
- 1
- References
- 20
- Related Works
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- OpenAlex ID
- https://openalex.org/W2895754887
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2895754887Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2018.2874010Digital Object Identifier
- Title
-
Quadratic Taylor Approximation Demosaicking System Using Post-Processing for Zipper Artifact RemovalWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
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2018-01-01Full publication date if available
- Authors
-
Daejun Park, Jechang JeongList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2018.2874010Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1109/access.2018.2874010Direct OA link when available
- Concepts
-
Demosaicing, Zipper, Artificial intelligence, Computer vision, Artifact (error), Interpolation (computer graphics), Pixel, Computer science, Mathematics, Image processing, Pattern recognition (psychology), Image (mathematics), Algorithm, Color imageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2021: 1Per-year citation counts (last 5 years)
- References (count)
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20Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.planes. | 128 |
| abstract_inverted_index.predict | 35 |
| abstract_inverted_index.prevent | 70 |
| abstract_inverted_index.process | 119 |
| abstract_inverted_index.propose | 4, 83 |
| abstract_inverted_index.removal | 90, 118 |
| abstract_inverted_index.results | 130 |
| abstract_inverted_index.accurate | 36 |
| abstract_inverted_index.artifact | 87, 104, 117 |
| abstract_inverted_index.compared | 145 |
| abstract_inverted_index.improper | 22 |
| abstract_inverted_index.improves | 137 |
| abstract_inverted_index.process. | 62 |
| abstract_inverted_index.proposed | 134 |
| abstract_inverted_index.weighted | 41 |
| abstract_inverted_index.artifacts | 16, 76 |
| abstract_inverted_index.detection | 88, 105 |
| abstract_inverted_index.luminance | 108 |
| abstract_inverted_index.objective | 140 |
| abstract_inverted_index.performed | 121 |
| abstract_inverted_index.primarily | 19 |
| abstract_inverted_index.qualities | 144 |
| abstract_inverted_index.resulting | 79 |
| abstract_inverted_index.technique | 12 |
| abstract_inverted_index.difference | 53, 101 |
| abstract_inverted_index.high-order | 32 |
| abstract_inverted_index.modelbased | 102 |
| abstract_inverted_index.noticeable | 100 |
| abstract_inverted_index.occurrence | 72 |
| abstract_inverted_index.performing | 97 |
| abstract_inverted_index.prediction | 49 |
| abstract_inverted_index.subjective | 142 |
| abstract_inverted_index.algorithms. | 151 |
| abstract_inverted_index.demonstrate | 131 |
| abstract_inverted_index.demosaicked | 94, 112 |
| abstract_inverted_index.Experimental | 129 |
| abstract_inverted_index.conventional | 148 |
| abstract_inverted_index.demosaicking | 7, 66, 135, 150 |
| abstract_inverted_index.approximation | 33 |
| abstract_inverted_index.average-based | 42 |
| abstract_inverted_index.interpolation | 23, 61 |
| abstract_inverted_index.well-designed | 65 |
| abstract_inverted_index.clamping-based | 115 |
| abstract_inverted_index.interpolation. | 45 |
| abstract_inverted_index.post-processing | 11, 84 |
| abstract_inverted_index.quincunx-sampled | 26 |
| abstract_inverted_index.state-of-the-art | 149 |
| cited_by_percentile_year.max | 93 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.46411331 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |