Image Water Ripple Detection Method Based on Constraint Convolution and Attention Mechanism Article Swipe
Wei Kang
,
Kun Zhou
,
Chenlei Xu
,
Hongfu Ma
,
Jingchai Chi
,
Fan Pan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2644/1/012011
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2644/1/012011
Noise can introduce irrelevant interference signals, reduce the signal-to-noise ratio of the image, weaken the contrast between the target and the background, and make it more difficult to detect the target in the image, thus increasing the difficulty of water ripple detection. Therefore, a method for image water ripple detection based on constraint convolution and attention mechanism is proposed. Using the attention mechanism for image denoising, the “attention map” is calculated from both channel and spatial aspects, and the calculated “attention map” is multiplied by the image feature map for adaptive feature learning to achieve image denoising processing. The convolutional neural network is used to extract the features of the input image. Based on feature extraction, the constrained convolution operation is applied to highlight the detailed features of water ripples. The features obtained from the constrained convolution operation are input into the support vector machine classifier for the classification and detection of water ripples. According to the relationship between the patterns and features learned by the classifier, whether the image belongs to the category of water ripples is judged, to achieve water ripple detection. The experimental results show that the proposed method has a good image denoising effect and water ripple detection effect.
Related Topics
Concepts
Artificial intelligence
Pattern recognition (psychology)
Computer science
Ripple
Feature extraction
Convolution (computer science)
Computer vision
Convolutional neural network
Feature (linguistics)
Kernel (algebra)
Noise (video)
Image processing
Image (mathematics)
Artificial neural network
Mathematics
Engineering
Combinatorics
Linguistics
Voltage
Philosophy
Electrical engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/2644/1/012011
- OA Status
- diamond
- References
- 8
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388979909
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388979909Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1742-6596/2644/1/012011Digital Object Identifier
- Title
-
Image Water Ripple Detection Method Based on Constraint Convolution and Attention MechanismWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-01Full publication date if available
- Authors
-
Wei Kang, Kun Zhou, Chenlei Xu, Hongfu Ma, Jingchai Chi, Fan PanList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/2644/1/012011Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1088/1742-6596/2644/1/012011Direct OA link when available
- Concepts
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Artificial intelligence, Pattern recognition (psychology), Computer science, Ripple, Feature extraction, Convolution (computer science), Computer vision, Convolutional neural network, Feature (linguistics), Kernel (algebra), Noise (video), Image processing, Image (mathematics), Artificial neural network, Mathematics, Engineering, Combinatorics, Linguistics, Voltage, Philosophy, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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8Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.raw_source_name | Journal of Physics: Conference Series |
| primary_location.landing_page_url | https://doi.org/10.1088/1742-6596/2644/1/012011 |
| publication_date | 2023-11-01 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W3214734021, https://openalex.org/W3024207815, https://openalex.org/W4223972622, https://openalex.org/W4200161046, https://openalex.org/W2944356404, https://openalex.org/W2990940706, https://openalex.org/W3154493004, https://openalex.org/W3007105961 |
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