Joint Optimization in Underwater Image Enhancement: A Training Framework Integrating Pixel-Level and Physical-Channel Techniques
In recent years, with the increasing interest in marine research, the need to collect and process clear underwater optical images has become crucial. However, underwater images suffer from the absorption and scattering effects of the environment. In this paper, we propose Hybrid Underwater Image Enh...
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| Format: | Article |
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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/10857292/ |
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| author | Ozan Demir Metin Aktas Ender M. Eksioglu |
| author_facet | Ozan Demir Metin Aktas Ender M. Eksioglu |
| author_sort | Ozan Demir |
| collection | DOAJ |
| description | In recent years, with the increasing interest in marine research, the need to collect and process clear underwater optical images has become crucial. However, underwater images suffer from the absorption and scattering effects of the environment. In this paper, we propose Hybrid Underwater Image Enhancement Network (HUWIE-Net), a novel deep learning-based underwater image enhancement framework consisting of three distinct sections, which include an Image-to-Image Module, a Physics-Informed Module and a Fusion Module. The training methodology of HUWIE-Net is designed to jointly optimize both pixel-level-based and physical-channel-based enhancement modules. In this framework, while Image-to-Image Module is used for color correction in pixel level, Physics-Informed Module is used for dehazing by exploiting the underwater image formation model which defines the deformations in the underwater light propagation channel. We also propose to use the joint loss function for both Image-to-Image Module and Physics-Informed Module to enforce the joint optimization for better underwater image enhancement performance. The results of experiments conducted with real-world underwater images show that the proposed model achieves improved performance compared to state-of-the-art methods. The code for the newly developed HUWIE-Net is available at <uri>https://github.com/UIE-Lab/HUWIE-Net</uri>. |
| format | Article |
| id | doaj-art-2b6350e7723c4dd0b1740a6d0b3fc254 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2b6350e7723c4dd0b1740a6d0b3fc2542025-02-06T00:00:37ZengIEEEIEEE Access2169-35362025-01-0113220742208510.1109/ACCESS.2025.353617310857292Joint Optimization in Underwater Image Enhancement: A Training Framework Integrating Pixel-Level and Physical-Channel TechniquesOzan Demir0https://orcid.org/0009-0003-6904-8432Metin Aktas1Ender M. Eksioglu2https://orcid.org/0000-0002-7869-4159Defense Systems Technologies Division, Aselsan Inc., Ankara, TürkiyeDefense Systems Technologies Division, Aselsan Inc., Ankara, TürkiyeDepartment of Electronics and Communication Engineering, Istanbul Technical University, Istanbul, TürkiyeIn recent years, with the increasing interest in marine research, the need to collect and process clear underwater optical images has become crucial. However, underwater images suffer from the absorption and scattering effects of the environment. In this paper, we propose Hybrid Underwater Image Enhancement Network (HUWIE-Net), a novel deep learning-based underwater image enhancement framework consisting of three distinct sections, which include an Image-to-Image Module, a Physics-Informed Module and a Fusion Module. The training methodology of HUWIE-Net is designed to jointly optimize both pixel-level-based and physical-channel-based enhancement modules. In this framework, while Image-to-Image Module is used for color correction in pixel level, Physics-Informed Module is used for dehazing by exploiting the underwater image formation model which defines the deformations in the underwater light propagation channel. We also propose to use the joint loss function for both Image-to-Image Module and Physics-Informed Module to enforce the joint optimization for better underwater image enhancement performance. The results of experiments conducted with real-world underwater images show that the proposed model achieves improved performance compared to state-of-the-art methods. The code for the newly developed HUWIE-Net is available at <uri>https://github.com/UIE-Lab/HUWIE-Net</uri>.https://ieeexplore.ieee.org/document/10857292/Underwater image enhancementdeep learningunderwater image formation modeldark channel priorphysics-informed deep networkjoint optimization |
| spellingShingle | Ozan Demir Metin Aktas Ender M. Eksioglu Joint Optimization in Underwater Image Enhancement: A Training Framework Integrating Pixel-Level and Physical-Channel Techniques IEEE Access Underwater image enhancement deep learning underwater image formation model dark channel prior physics-informed deep network joint optimization |
| title | Joint Optimization in Underwater Image Enhancement: A Training Framework Integrating Pixel-Level and Physical-Channel Techniques |
| title_full | Joint Optimization in Underwater Image Enhancement: A Training Framework Integrating Pixel-Level and Physical-Channel Techniques |
| title_fullStr | Joint Optimization in Underwater Image Enhancement: A Training Framework Integrating Pixel-Level and Physical-Channel Techniques |
| title_full_unstemmed | Joint Optimization in Underwater Image Enhancement: A Training Framework Integrating Pixel-Level and Physical-Channel Techniques |
| title_short | Joint Optimization in Underwater Image Enhancement: A Training Framework Integrating Pixel-Level and Physical-Channel Techniques |
| title_sort | joint optimization in underwater image enhancement a training framework integrating pixel level and physical channel techniques |
| topic | Underwater image enhancement deep learning underwater image formation model dark channel prior physics-informed deep network joint optimization |
| url | https://ieeexplore.ieee.org/document/10857292/ |
| work_keys_str_mv | AT ozandemir jointoptimizationinunderwaterimageenhancementatrainingframeworkintegratingpixellevelandphysicalchanneltechniques AT metinaktas jointoptimizationinunderwaterimageenhancementatrainingframeworkintegratingpixellevelandphysicalchanneltechniques AT endermeksioglu jointoptimizationinunderwaterimageenhancementatrainingframeworkintegratingpixellevelandphysicalchanneltechniques |