ICAFormer: An Image Dehazing Transformer Based on Interactive Channel Attention
Single image dehazing is a fundamental task in computer vision, aiming to recover a clear scene from a hazy input image. To address the limitations of traditional dehazing algorithms—particularly in global feature association and local detail preservation—this study proposes a novel Transformer-base...
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| Language: | English |
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/12/3750 |
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| author | Yanfei Chen Tong Yue Pei An Hanyu Hong Tao Liu Yangkai Liu Yihui Zhou |
| author_facet | Yanfei Chen Tong Yue Pei An Hanyu Hong Tao Liu Yangkai Liu Yihui Zhou |
| author_sort | Yanfei Chen |
| collection | DOAJ |
| description | Single image dehazing is a fundamental task in computer vision, aiming to recover a clear scene from a hazy input image. To address the limitations of traditional dehazing algorithms—particularly in global feature association and local detail preservation—this study proposes a novel Transformer-based dehazing model enhanced by an interactive channel attention mechanism. The proposed architecture adopts a U-shaped encoder–decoder framework, incorporating key components such as a feature extraction module and a feature fusion module based on interactive attention. Specifically, the interactive channel attention mechanism facilitates cross-layer feature interaction, enabling the dynamic fusion of global contextual information and local texture details. The network architecture leverages a multi-scale feature pyramid to extract image information across different dimensions, while an improved cross-channel attention weighting mechanism enhances feature representation in regions with varying haze densities. Extensive experiments conducted on both synthetic and real-world datasets—including the RESIDE benchmark—demonstrate the superior performance of the proposed method. Quantitatively, it achieves PSNR gains of 0.53 dB for indoor scenes and 1.64 dB for outdoor scenes, alongside SSIM improvements of 1.4% and 1.7%, respectively, compared with the second-best performing method. Qualitative assessments further confirm that the proposed model excels in restoring fine structural details in dense haze regions while maintaining high color fidelity. These results validate the effectiveness of the proposed approach in enhancing both perceptual quality and quantitative accuracy in image dehazing tasks. |
| format | Article |
| id | doaj-art-718ff276b4f5427c8a816b43d058f46c |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-718ff276b4f5427c8a816b43d058f46c2025-08-20T03:26:51ZengMDPI AGSensors1424-82202025-06-012512375010.3390/s25123750ICAFormer: An Image Dehazing Transformer Based on Interactive Channel AttentionYanfei Chen0Tong Yue1Pei An2Hanyu Hong3Tao Liu4Yangkai Liu5Yihui Zhou6Hubei Key Laboratory of Optical Information and Pattern Recognition, School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaHubei Key Laboratory of Optical Information and Pattern Recognition, School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaSchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430072, ChinaHubei Key Laboratory of Optical Information and Pattern Recognition, School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaHubei Key Laboratory of Optical Information and Pattern Recognition, School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaHubei Key Laboratory of Optical Information and Pattern Recognition, School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaHubei Key Laboratory of Optical Information and Pattern Recognition, School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaSingle image dehazing is a fundamental task in computer vision, aiming to recover a clear scene from a hazy input image. To address the limitations of traditional dehazing algorithms—particularly in global feature association and local detail preservation—this study proposes a novel Transformer-based dehazing model enhanced by an interactive channel attention mechanism. The proposed architecture adopts a U-shaped encoder–decoder framework, incorporating key components such as a feature extraction module and a feature fusion module based on interactive attention. Specifically, the interactive channel attention mechanism facilitates cross-layer feature interaction, enabling the dynamic fusion of global contextual information and local texture details. The network architecture leverages a multi-scale feature pyramid to extract image information across different dimensions, while an improved cross-channel attention weighting mechanism enhances feature representation in regions with varying haze densities. Extensive experiments conducted on both synthetic and real-world datasets—including the RESIDE benchmark—demonstrate the superior performance of the proposed method. Quantitatively, it achieves PSNR gains of 0.53 dB for indoor scenes and 1.64 dB for outdoor scenes, alongside SSIM improvements of 1.4% and 1.7%, respectively, compared with the second-best performing method. Qualitative assessments further confirm that the proposed model excels in restoring fine structural details in dense haze regions while maintaining high color fidelity. These results validate the effectiveness of the proposed approach in enhancing both perceptual quality and quantitative accuracy in image dehazing tasks.https://www.mdpi.com/1424-8220/25/12/3750image dehazeTransformerfeature extractionattention mechanism |
| spellingShingle | Yanfei Chen Tong Yue Pei An Hanyu Hong Tao Liu Yangkai Liu Yihui Zhou ICAFormer: An Image Dehazing Transformer Based on Interactive Channel Attention Sensors image dehaze Transformer feature extraction attention mechanism |
| title | ICAFormer: An Image Dehazing Transformer Based on Interactive Channel Attention |
| title_full | ICAFormer: An Image Dehazing Transformer Based on Interactive Channel Attention |
| title_fullStr | ICAFormer: An Image Dehazing Transformer Based on Interactive Channel Attention |
| title_full_unstemmed | ICAFormer: An Image Dehazing Transformer Based on Interactive Channel Attention |
| title_short | ICAFormer: An Image Dehazing Transformer Based on Interactive Channel Attention |
| title_sort | icaformer an image dehazing transformer based on interactive channel attention |
| topic | image dehaze Transformer feature extraction attention mechanism |
| url | https://www.mdpi.com/1424-8220/25/12/3750 |
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