ODD-Net: a hybrid deep learning architecture for image dehazing
Abstract Haze can significantly reduce visibility and contrast of images captured outdoors, necessitating the enhancement of images. This degradation in image quality can adversely affect various applications, including autonomous driving, object detection, and surveillance, where poor visibility ma...
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-82558-6 |
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| author | C. S. Asha Abu Bakr Siddiq Razeem Akthar M. Ragesh Rajan Shilpa Suresh |
| author_facet | C. S. Asha Abu Bakr Siddiq Razeem Akthar M. Ragesh Rajan Shilpa Suresh |
| author_sort | C. S. Asha |
| collection | DOAJ |
| description | Abstract Haze can significantly reduce visibility and contrast of images captured outdoors, necessitating the enhancement of images. This degradation in image quality can adversely affect various applications, including autonomous driving, object detection, and surveillance, where poor visibility may lead to navigation errors and obscure crucial details. Existing dehazing techniques face several challenges: spatial methods tend to be computationally heavy, transform methods often fall short in quality, hybrid methods can be intricate and demanding, and deep learning methods require extensive datasets and computational power. To overcome these challenges, we present ODD-Net, a hybrid deep learning architecture. Our research introduces a comprehensive data set and an innovative architecture called Atmospheric Light Net (A-Net) to estimate atmospheric light, using dilated convolution, batch normalisation, and ReLU activation functions. Furthermore, we develop T-Net to measure information transmission from objects to the camera, using multiscale convolutions and nonlinear regression to create a transmission map. The integrated architecture combines pre-trained A-Net and T-Net models within the atmospheric scattering model. Comparative analysis shows that ODD-Net provides superior dehazing quality, especially in transmission map estimation and depth measurement, surpassing state-of-the-art methods such as DCP, GMAN, DehazeNet, and LCA. Our quantitative analysis reveals that ODD-Net achieves the highest performance in terms the quality metrics compared. The proposed method demonstrates notable quantitative and qualitative improvements over existing techniques, setting a new standard in image dehazing. |
| format | Article |
| id | doaj-art-f035fbac49d042e4a87f0e93990093ff |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f035fbac49d042e4a87f0e93990093ff2025-08-20T02:28:04ZengNature PortfolioScientific Reports2045-23222024-12-0114111710.1038/s41598-024-82558-6ODD-Net: a hybrid deep learning architecture for image dehazingC. S. Asha0Abu Bakr Siddiq1Razeem Akthar2M. Ragesh Rajan3Shilpa Suresh4Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher EducationDepartment of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher EducationDepartment of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher EducationDepartment of Electronics and Communication Engineering, Amrita Vishwa VidyapeethamDepartment of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher EducationAbstract Haze can significantly reduce visibility and contrast of images captured outdoors, necessitating the enhancement of images. This degradation in image quality can adversely affect various applications, including autonomous driving, object detection, and surveillance, where poor visibility may lead to navigation errors and obscure crucial details. Existing dehazing techniques face several challenges: spatial methods tend to be computationally heavy, transform methods often fall short in quality, hybrid methods can be intricate and demanding, and deep learning methods require extensive datasets and computational power. To overcome these challenges, we present ODD-Net, a hybrid deep learning architecture. Our research introduces a comprehensive data set and an innovative architecture called Atmospheric Light Net (A-Net) to estimate atmospheric light, using dilated convolution, batch normalisation, and ReLU activation functions. Furthermore, we develop T-Net to measure information transmission from objects to the camera, using multiscale convolutions and nonlinear regression to create a transmission map. The integrated architecture combines pre-trained A-Net and T-Net models within the atmospheric scattering model. Comparative analysis shows that ODD-Net provides superior dehazing quality, especially in transmission map estimation and depth measurement, surpassing state-of-the-art methods such as DCP, GMAN, DehazeNet, and LCA. Our quantitative analysis reveals that ODD-Net achieves the highest performance in terms the quality metrics compared. The proposed method demonstrates notable quantitative and qualitative improvements over existing techniques, setting a new standard in image dehazing.https://doi.org/10.1038/s41598-024-82558-6Image dehazingDeep retinexLCADense depthA NetT Net |
| spellingShingle | C. S. Asha Abu Bakr Siddiq Razeem Akthar M. Ragesh Rajan Shilpa Suresh ODD-Net: a hybrid deep learning architecture for image dehazing Scientific Reports Image dehazing Deep retinex LCA Dense depth A Net T Net |
| title | ODD-Net: a hybrid deep learning architecture for image dehazing |
| title_full | ODD-Net: a hybrid deep learning architecture for image dehazing |
| title_fullStr | ODD-Net: a hybrid deep learning architecture for image dehazing |
| title_full_unstemmed | ODD-Net: a hybrid deep learning architecture for image dehazing |
| title_short | ODD-Net: a hybrid deep learning architecture for image dehazing |
| title_sort | odd net a hybrid deep learning architecture for image dehazing |
| topic | Image dehazing Deep retinex LCA Dense depth A Net T Net |
| url | https://doi.org/10.1038/s41598-024-82558-6 |
| work_keys_str_mv | AT csasha oddnetahybriddeeplearningarchitectureforimagedehazing AT abubakrsiddiq oddnetahybriddeeplearningarchitectureforimagedehazing AT razeemakthar oddnetahybriddeeplearningarchitectureforimagedehazing AT mrageshrajan oddnetahybriddeeplearningarchitectureforimagedehazing AT shilpasuresh oddnetahybriddeeplearningarchitectureforimagedehazing |