A bi-stream transformer for single-image dehazing
Deep-learning methods, such as encoder-decoder networks, have achieved impressive results in image dehazing. However, these methods often rely only on synthesized data for training that limits their generalizability to hazy, real-world images. To leverage prior knowledge of haze properties, we propo...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Electronics and Telecommunications Research Institute (ETRI)
2025-06-01
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| Series: | ETRI Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.4218/etrij.2024-0037 |
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| Summary: | Deep-learning methods, such as encoder-decoder networks, have achieved impressive results in image dehazing. However, these methods often rely only on synthesized data for training that limits their generalizability to hazy, real-world images. To leverage prior knowledge of haze properties, we propose a bi-encoder structure that integrates a prior-based encoder into a traditional encoder-decoder network. The features from both encoders were fused using a feature enhancement module. We adopted transformer blocks instead of convolutions to model local feature associations. Experimen-tal results demonstrate that our method surpasses state-of-the-art methods for synthesized and actual hazy scenes. Therefore, we believe that our method will be a useful supplement to the collection of current artificial intelligence models and will benefit engineering applications in computer vision.
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| ISSN: | 1225-6463 2233-7326 |