Dual-CycleGANs with Dynamic Guidance for Robust Underwater Image Restoration
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| Publicado en: | Journal of Marine Science and Engineering vol. 13, no. 2 (2025), p. 231 |
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| Autor principal: | |
| Otros Autores: | , |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3171120121 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2077-1312 | ||
| 024 | 7 | |a 10.3390/jmse13020231 |2 doi | |
| 035 | |a 3171120121 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231479 |2 nlm | ||
| 100 | 1 | |a Yu-Yang, Lin |u Institute of Communications Engineering, National Sun Yat-Sen University, Kaohsiung 80404, Taiwan<email>wjhuang@faculty.nsysu.edu.tw</email> (W.-J.H.) | |
| 245 | 1 | |a Dual-CycleGANs with Dynamic Guidance for Robust Underwater Image Restoration | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The field of underwater image processing has gained significant attention recently, offering great potential for enhanced exploration of underwater environments, including applications such as underwater terrain scanning and autonomous underwater vehicles. However, underwater images frequently face challenges such as light attenuation, color distortion, and noise introduced by artificial light sources. These degradations not only affect image quality but also hinder the effectiveness of related application tasks. To address these issues, this paper presents a novel deep network model for single under-water image restoration. Our model does not rely on paired training images and incorporates two cycle-consistent generative adversarial network (CycleGAN) structures, forming a dual-CycleGAN architecture. This enables the simultaneous conversion of an underwater image to its in-air (atmospheric) counterpart while learning a light field image to guide the underwater image towards its in-air version. Experimental results indicate that the proposed method provides superior (or at least comparable) image restoration performance, both in terms of quantitative measures and visual quality, when compared to existing state-of-the-art techniques. Our model significantly reduces computational complexity, resulting in a more efficient approach that maintains superior restoration capabilities, ensuring faster processing times and lower memory usage, making it highly suitable for real-world applications. | |
| 653 | |a Autonomous underwater vehicles | ||
| 653 | |a Datasets | ||
| 653 | |a Deep learning | ||
| 653 | |a Photodegradation | ||
| 653 | |a Light sources | ||
| 653 | |a Underwater exploration | ||
| 653 | |a Neural networks | ||
| 653 | |a Generative adversarial networks | ||
| 653 | |a Image processing | ||
| 653 | |a Image restoration | ||
| 653 | |a Underwater vehicles | ||
| 653 | |a Methods | ||
| 653 | |a Light attenuation | ||
| 653 | |a Image quality | ||
| 653 | |a Light | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Wan-Jen, Huang |u Institute of Communications Engineering, National Sun Yat-Sen University, Kaohsiung 80404, Taiwan<email>wjhuang@faculty.nsysu.edu.tw</email> (W.-J.H.) | |
| 700 | 1 | |a Yeh, Chia-Hung |u Department of Electrical Engineering, National Taiwan Normal University, Taipei 10610, Taiwan; Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80404, Taiwan | |
| 773 | 0 | |t Journal of Marine Science and Engineering |g vol. 13, no. 2 (2025), p. 231 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3171120121/abstract/embedded/160PP4OP4BJVV2EV?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3171120121/fulltextwithgraphics/embedded/160PP4OP4BJVV2EV?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3171120121/fulltextPDF/embedded/160PP4OP4BJVV2EV?source=fedsrch |