Underwater Image Enhancement Based on Transformer, Attention, and Multi-Color-Space Inputs
Underwater images suffer from color distortion, low contrast and blurring caused by the attenuation, refraction, and scattering of light. For many maritime operations, underwater image enhancement is essential. This paper proposes an underwater image enhancement method based on transformer, attentio...
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| Main Authors: | , , , , |
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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/11025830/ |
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| author | Liqiong Lu Dong Wu Liuyin Wang Wanzhen Zhang Tonglai Liu |
| author_facet | Liqiong Lu Dong Wu Liuyin Wang Wanzhen Zhang Tonglai Liu |
| author_sort | Liqiong Lu |
| collection | DOAJ |
| description | Underwater images suffer from color distortion, low contrast and blurring caused by the attenuation, refraction, and scattering of light. For many maritime operations, underwater image enhancement is essential. This paper proposes an underwater image enhancement method based on transformer, attention and multi-color-space inputs. First, transformer block is embedded into ResNet-50 as the backbone network. Combining this backbone network and multi-scale feature fusion forms the fundamental framework of our method. This framework contributes to mine the rich features of different scene underwater image and outputs a feature map of the same size as the input image. Then, multi-dimensional attention mechanism is added to the feature maps of different scales to mine key areas that require enhancement in underwater images and calculate the enhancement value of each pixel. Finally, multi-color-space inputs including RGB, HSV and LAB as the inputs for CNN to enhancement images from various aspects such as color deviation, brightness and saturation. The comparison of image quality evaluation metrics, visual enhancement performance evaluation and impact on the performance of underwater object detection with other underwater enhancement methods on datasets including SUIM-E, UIEB, EUVP, RUIE-RIQS, RUIE-UCCS and URPC-2018 prove the good performance of our underwater image enhancement method. |
| format | Article |
| id | doaj-art-5a94105c9feb443e89d2e323a4337e01 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5a94105c9feb443e89d2e323a4337e012025-08-20T02:07:57ZengIEEEIEEE Access2169-35362025-01-011310368210369610.1109/ACCESS.2025.357700511025830Underwater Image Enhancement Based on Transformer, Attention, and Multi-Color-Space InputsLiqiong Lu0https://orcid.org/0000-0003-0413-2097Dong Wu1Liuyin Wang2Wanzhen Zhang3Tonglai Liu4https://orcid.org/0000-0002-1845-5623School of Computer Science and Intelligence Education, Lingnan Normal University, Zhanjiang, ChinaSchool of Computer Science and Intelligence Education, Lingnan Normal University, Zhanjiang, ChinaSchool of Computer Science and Intelligence Education, Lingnan Normal University, Zhanjiang, ChinaCollege of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, ChinaCollege of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, ChinaUnderwater images suffer from color distortion, low contrast and blurring caused by the attenuation, refraction, and scattering of light. For many maritime operations, underwater image enhancement is essential. This paper proposes an underwater image enhancement method based on transformer, attention and multi-color-space inputs. First, transformer block is embedded into ResNet-50 as the backbone network. Combining this backbone network and multi-scale feature fusion forms the fundamental framework of our method. This framework contributes to mine the rich features of different scene underwater image and outputs a feature map of the same size as the input image. Then, multi-dimensional attention mechanism is added to the feature maps of different scales to mine key areas that require enhancement in underwater images and calculate the enhancement value of each pixel. Finally, multi-color-space inputs including RGB, HSV and LAB as the inputs for CNN to enhancement images from various aspects such as color deviation, brightness and saturation. The comparison of image quality evaluation metrics, visual enhancement performance evaluation and impact on the performance of underwater object detection with other underwater enhancement methods on datasets including SUIM-E, UIEB, EUVP, RUIE-RIQS, RUIE-UCCS and URPC-2018 prove the good performance of our underwater image enhancement method.https://ieeexplore.ieee.org/document/11025830/Underwater image enhancementtransformer blockCNNResNet-50multi-dimensional attention mechanismmulti-color-space inputs |
| spellingShingle | Liqiong Lu Dong Wu Liuyin Wang Wanzhen Zhang Tonglai Liu Underwater Image Enhancement Based on Transformer, Attention, and Multi-Color-Space Inputs IEEE Access Underwater image enhancement transformer block CNN ResNet-50 multi-dimensional attention mechanism multi-color-space inputs |
| title | Underwater Image Enhancement Based on Transformer, Attention, and Multi-Color-Space Inputs |
| title_full | Underwater Image Enhancement Based on Transformer, Attention, and Multi-Color-Space Inputs |
| title_fullStr | Underwater Image Enhancement Based on Transformer, Attention, and Multi-Color-Space Inputs |
| title_full_unstemmed | Underwater Image Enhancement Based on Transformer, Attention, and Multi-Color-Space Inputs |
| title_short | Underwater Image Enhancement Based on Transformer, Attention, and Multi-Color-Space Inputs |
| title_sort | underwater image enhancement based on transformer attention and multi color space inputs |
| topic | Underwater image enhancement transformer block CNN ResNet-50 multi-dimensional attention mechanism multi-color-space inputs |
| url | https://ieeexplore.ieee.org/document/11025830/ |
| work_keys_str_mv | AT liqionglu underwaterimageenhancementbasedontransformerattentionandmulticolorspaceinputs AT dongwu underwaterimageenhancementbasedontransformerattentionandmulticolorspaceinputs AT liuyinwang underwaterimageenhancementbasedontransformerattentionandmulticolorspaceinputs AT wanzhenzhang underwaterimageenhancementbasedontransformerattentionandmulticolorspaceinputs AT tonglailiu underwaterimageenhancementbasedontransformerattentionandmulticolorspaceinputs |