MSFE-UIENet: A Multi-Scale Feature Extraction Network for Marine Underwater Image Enhancement Article Swipe
Shengya Zhao
,
Xinkui Mei
,
Xiufen Ye
,
Shuxiang Guo
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/jmse12091472
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/jmse12091472
Underwater optical images have outstanding advantages for short-range underwater target detection tasks. However, owing to the limitations of special underwater imaging environments, underwater images often have several problems, such as noise interference, blur texture, low contrast, and color distortion. Marine underwater image enhancement addresses degraded underwater image quality caused by light absorption and scattering. This study introduces MSFE-UIENet, a high-performance network designed to improve image feature extraction, resulting in deep-learning-based underwater image enhancement, addressing the limitations of single convolution and upsampling/downsampling techniques. This network is designed to enhance the image quality in underwater settings by employing an encoder–decoder architecture. In response to the underwhelming enhancement performance caused by the conventional networks’ sole downsampling method, this study introduces a pyramid downsampling module that captures more intricate image features through multi-scale downsampling. Additionally, to augment the feature extraction capabilities of the network, an advanced feature extraction module was proposed to capture detailed information from underwater images. Furthermore, to optimize the network’s gradient flow, forward and backward branches were introduced to accelerate its convergence rate and improve stability. Experimental validation using underwater image datasets indicated that the proposed network effectively enhances underwater image quality, effectively preserving image details and noise suppression across various underwater environments.
Related Topics
Concepts
Underwater
Image enhancement
Scale (ratio)
Feature extraction
Feature (linguistics)
Extraction (chemistry)
Environmental science
Image (mathematics)
Computer science
Artificial intelligence
Geology
Pattern recognition (psychology)
Oceanography
Geography
Cartography
Chemistry
Chromatography
Linguistics
Philosophy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jmse12091472
- https://www.mdpi.com/2077-1312/12/9/1472/pdf?version=1724420687
- OA Status
- gold
- Cited By
- 4
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401806502
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401806502Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/jmse12091472Digital Object Identifier
- Title
-
MSFE-UIENet: A Multi-Scale Feature Extraction Network for Marine Underwater Image EnhancementWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-23Full publication date if available
- Authors
-
Shengya Zhao, Xinkui Mei, Xiufen Ye, Shuxiang GuoList of authors in order
- Landing page
-
https://doi.org/10.3390/jmse12091472Publisher landing page
- PDF URL
-
https://www.mdpi.com/2077-1312/12/9/1472/pdf?version=1724420687Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2077-1312/12/9/1472/pdf?version=1724420687Direct OA link when available
- Concepts
-
Underwater, Image enhancement, Scale (ratio), Feature extraction, Feature (linguistics), Extraction (chemistry), Environmental science, Image (mathematics), Computer science, Artificial intelligence, Geology, Pattern recognition (psychology), Oceanography, Geography, Cartography, Chemistry, Chromatography, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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