Non-Homogeneous Image Dehazing Model Based on Vision Transformer Shunt Self-Attention Aggregation
This study introduces an innovative dehazing technique utilizing a Vision Transformer to mitigate the image quality degradation caused by non-homogeneous haze in real-world environments. Initially, the model employs convolutional layers to augment the channel dimensions of the input image, enabling...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11028993/ |
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| author | Zhigang Zhang Byung-Won Min Zijiao Zhang |
| author_facet | Zhigang Zhang Byung-Won Min Zijiao Zhang |
| author_sort | Zhigang Zhang |
| collection | DOAJ |
| description | This study introduces an innovative dehazing technique utilizing a Vision Transformer to mitigate the image quality degradation caused by non-homogeneous haze in real-world environments. Initially, the model employs convolutional layers to augment the channel dimensions of the input image, enabling the extraction of basic local features. Subsequently, we incorporate a Multi-Scale Channel-Pixel Joint Attention Module, which refines the attention on various haze-affected areas, ensuring accurate capture of the complex characteristics associated with non-homogeneous haze. The cornerstone of our approach is the proposed improved Vision Transformer, which integrates a Shunt Self-Attention Aggregation Module. This module, leveraging a multi-head parallel self-attention mechanism, facilitates the efficient fusion of features across multiple scales, thereby enhancing feature reuse and integration. We conducted extensive experimental evaluations using both real-world and synthetic non-homogeneous haze datasets, rigorously validating the robustness and effectiveness of our model. The results demonstrate that our approach surpasses existing benchmark techniques across various evaluation metrics, affirming its preeminence in the field of image dehazing. |
| format | Article |
| id | doaj-art-5cbcb939e4074353ab633194f4cdaaaa |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5cbcb939e4074353ab633194f4cdaaaa2025-08-20T03:31:21ZengIEEEIEEE Access2169-35362025-01-011310114310115910.1109/ACCESS.2025.357819511028993Non-Homogeneous Image Dehazing Model Based on Vision Transformer Shunt Self-Attention AggregationZhigang Zhang0https://orcid.org/0000-0002-4929-2169Byung-Won Min1Zijiao Zhang2College of Artificial Intelligence, Jiaozuo University, Jiaozuo, ChinaDepartment of IT Engineering, Mokwon University, Daejeon, South KoreaSchool of Telecommunications and Intelligent Manufacturing, Sias University, Zhengzhou, ChinaThis study introduces an innovative dehazing technique utilizing a Vision Transformer to mitigate the image quality degradation caused by non-homogeneous haze in real-world environments. Initially, the model employs convolutional layers to augment the channel dimensions of the input image, enabling the extraction of basic local features. Subsequently, we incorporate a Multi-Scale Channel-Pixel Joint Attention Module, which refines the attention on various haze-affected areas, ensuring accurate capture of the complex characteristics associated with non-homogeneous haze. The cornerstone of our approach is the proposed improved Vision Transformer, which integrates a Shunt Self-Attention Aggregation Module. This module, leveraging a multi-head parallel self-attention mechanism, facilitates the efficient fusion of features across multiple scales, thereby enhancing feature reuse and integration. We conducted extensive experimental evaluations using both real-world and synthetic non-homogeneous haze datasets, rigorously validating the robustness and effectiveness of our model. The results demonstrate that our approach surpasses existing benchmark techniques across various evaluation metrics, affirming its preeminence in the field of image dehazing.https://ieeexplore.ieee.org/document/11028993/Features reusenon-homogeneous dehazingmulti-scale channel-pixel joint attentionvision transformer shunt self-attention aggregation |
| spellingShingle | Zhigang Zhang Byung-Won Min Zijiao Zhang Non-Homogeneous Image Dehazing Model Based on Vision Transformer Shunt Self-Attention Aggregation IEEE Access Features reuse non-homogeneous dehazing multi-scale channel-pixel joint attention vision transformer shunt self-attention aggregation |
| title | Non-Homogeneous Image Dehazing Model Based on Vision Transformer Shunt Self-Attention Aggregation |
| title_full | Non-Homogeneous Image Dehazing Model Based on Vision Transformer Shunt Self-Attention Aggregation |
| title_fullStr | Non-Homogeneous Image Dehazing Model Based on Vision Transformer Shunt Self-Attention Aggregation |
| title_full_unstemmed | Non-Homogeneous Image Dehazing Model Based on Vision Transformer Shunt Self-Attention Aggregation |
| title_short | Non-Homogeneous Image Dehazing Model Based on Vision Transformer Shunt Self-Attention Aggregation |
| title_sort | non homogeneous image dehazing model based on vision transformer shunt self attention aggregation |
| topic | Features reuse non-homogeneous dehazing multi-scale channel-pixel joint attention vision transformer shunt self-attention aggregation |
| url | https://ieeexplore.ieee.org/document/11028993/ |
| work_keys_str_mv | AT zhigangzhang nonhomogeneousimagedehazingmodelbasedonvisiontransformershuntselfattentionaggregation AT byungwonmin nonhomogeneousimagedehazingmodelbasedonvisiontransformershuntselfattentionaggregation AT zijiaozhang nonhomogeneousimagedehazingmodelbasedonvisiontransformershuntselfattentionaggregation |