UNIR-Net: A Novel Approach for Restoring Underwater Images with Non-Uniform Illumination Using Synthetic Data
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915518034739200 |
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| author | Perez-Zarate, Ezequiel Liu, Chunxiao Ramos-Soto, Oscar Oliva, Diego Perez-Cisneros, Marco |
| author_facet | Perez-Zarate, Ezequiel Liu, Chunxiao Ramos-Soto, Oscar Oliva, Diego Perez-Cisneros, Marco |
| contents | Restoring underwater images affected by non-uniform illumination (NUI) is essential to improve visual quality and usability in marine applications. Conventional methods often fall short in handling complex illumination patterns, while learning-based approaches face challenges due to the lack of targeted datasets. To address these limitations, the Underwater Non-uniform Illumination Restoration Network (UNIR-Net) is proposed. UNIR-Net integrates multiple components, including illumination enhancement, attention mechanisms, visual refinement, and contrast correction, to effectively restore underwater images affected by NUI. In addition, the Paired Underwater Non-uniform Illumination (PUNI) dataset is introduced, specifically designed for training and evaluating models under NUI conditions. Experimental results on PUNI and the large-scale real-world Non-Uniform Illumination Dataset (NUID) show that UNIR-Net achieves superior performance in both quantitative metrics and visual outcomes. UNIR-Net also improves downstream tasks such as underwater semantic segmentation, highlighting its practical relevance. The code of this method is available at https://github.com/xingyumex/UNIR-Net |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09053 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UNIR-Net: A Novel Approach for Restoring Underwater Images with Non-Uniform Illumination Using Synthetic Data Perez-Zarate, Ezequiel Liu, Chunxiao Ramos-Soto, Oscar Oliva, Diego Perez-Cisneros, Marco Image and Video Processing Restoring underwater images affected by non-uniform illumination (NUI) is essential to improve visual quality and usability in marine applications. Conventional methods often fall short in handling complex illumination patterns, while learning-based approaches face challenges due to the lack of targeted datasets. To address these limitations, the Underwater Non-uniform Illumination Restoration Network (UNIR-Net) is proposed. UNIR-Net integrates multiple components, including illumination enhancement, attention mechanisms, visual refinement, and contrast correction, to effectively restore underwater images affected by NUI. In addition, the Paired Underwater Non-uniform Illumination (PUNI) dataset is introduced, specifically designed for training and evaluating models under NUI conditions. Experimental results on PUNI and the large-scale real-world Non-Uniform Illumination Dataset (NUID) show that UNIR-Net achieves superior performance in both quantitative metrics and visual outcomes. UNIR-Net also improves downstream tasks such as underwater semantic segmentation, highlighting its practical relevance. The code of this method is available at https://github.com/xingyumex/UNIR-Net |
| title | UNIR-Net: A Novel Approach for Restoring Underwater Images with Non-Uniform Illumination Using Synthetic Data |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2501.09053 |