Generic Model-Agnostic Convolutional Neural Networks for Single Image Dehazing Article Swipe
Haze and smog are among the most common environmental factors impacting image quality and, therefore, image analysis. In this paper, I propose an end-to-end generative method for single image dehazing problem. It is based on fully convolutional network and effective network structures to recognize haze structure in input images and restore clear, haze-free ones. The proposed method is agnostic in the sense that it does not explore the atmosphere scattering model, it makes use of convolutional networks advantage in feature extraction and transfer instead. Somewhat surprisingly, it achieves superior performance relative to all existing state-of-the-art methods for image dehazing even on SOTS outdoor images, which are synthesized using the atmosphere scattering model. In order to improve its weakness in indoor hazy images and enhance the dehazed image's visual quality, a lightweight parallel network is put forward. It employs a different convolution strategy that extracts features with larger reception field to generate a complementary image. With the help of a parallel stream, the fusion of the two outputs performs better in PSNR and SSIM than other methods.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://hdl.handle.net/11375/23979
- http://hdl.handle.net/11375/23979
- OA Status
- green
- Cited By
- 2
- References
- 20
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2894862560
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2894862560Canonical identifier for this work in OpenAlex
- Title
-
Generic Model-Agnostic Convolutional Neural Networks for Single Image DehazingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-10-05Full publication date if available
- Authors
-
Zheng Liu, Botao Xiao, Muhammad Alrabeiah, Keyan Wang, Jun ChenList of authors in order
- Landing page
-
https://hdl.handle.net/11375/23979Publisher landing page
- PDF URL
-
https://hdl.handle.net/11375/23979Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://hdl.handle.net/11375/23979Direct OA link when available
- Concepts
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Computer science, Haze, Image (mathematics), Convolutional neural network, Atmosphere (unit), Code (set theory), Artificial intelligence, Image restoration, Computer vision, Pattern recognition (psychology), Image processing, Geography, Set (abstract data type), Meteorology, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2021: 2Per-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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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.existing | 93 |
| abstract_inverted_index.extracts | 143 |
| abstract_inverted_index.features | 144 |
| abstract_inverted_index.forward. | 135 |
| abstract_inverted_index.generate | 150 |
| abstract_inverted_index.instead. | 83 |
| abstract_inverted_index.methods. | 175 |
| abstract_inverted_index.networks | 76 |
| abstract_inverted_index.parallel | 131, 159 |
| abstract_inverted_index.performs | 167 |
| abstract_inverted_index.problem. | 30 |
| abstract_inverted_index.proposed | 55 |
| abstract_inverted_index.quality, | 128 |
| abstract_inverted_index.relative | 90 |
| abstract_inverted_index.strategy | 141 |
| abstract_inverted_index.superior | 88 |
| abstract_inverted_index.transfer | 82 |
| abstract_inverted_index.weakness | 117 |
| abstract_inverted_index.advantage | 77 |
| abstract_inverted_index.analysis. | 16 |
| abstract_inverted_index.different | 139 |
| abstract_inverted_index.effective | 39 |
| abstract_inverted_index.haze-free | 52 |
| abstract_inverted_index.impacting | 10 |
| abstract_inverted_index.reception | 147 |
| abstract_inverted_index.recognize | 43 |
| abstract_inverted_index.structure | 45 |
| abstract_inverted_index.atmosphere | 68, 109 |
| abstract_inverted_index.end-to-end | 23 |
| abstract_inverted_index.extraction | 80 |
| abstract_inverted_index.generative | 24 |
| abstract_inverted_index.scattering | 69, 110 |
| abstract_inverted_index.structures | 41 |
| abstract_inverted_index.therefore, | 14 |
| abstract_inverted_index.convolution | 140 |
| abstract_inverted_index.lightweight | 130 |
| abstract_inverted_index.performance | 89 |
| abstract_inverted_index.synthesized | 106 |
| abstract_inverted_index.complementary | 152 |
| abstract_inverted_index.convolutional | 36, 75 |
| abstract_inverted_index.environmental | 8 |
| abstract_inverted_index.surprisingly, | 85 |
| abstract_inverted_index.state-of-the-art | 94 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.5600000023841858 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile |