دورية أكاديمية
Comparison of GAN Deep Learning Methods for Underwater Optical Image Enhancement
| العنوان: | Comparison of GAN Deep Learning Methods for Underwater Optical Image Enhancement |
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| المؤلفون: | Hong-Gi Kim, Jung-Min Seo, Soo Mee Kim |
| المصدر: | 한국해양공학회지, Vol 36, Iss 1, Pp 32-40 (2022) |
| بيانات النشر: | The Korean Society of Ocean Engineers, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Ocean engineering |
| مصطلحات موضوعية: | generative adversarial networks, image fusion, image enhancement, underwater optical image, underwater image deep learning techniques, Ocean engineering, TC1501-1800 |
| الوصف: | Underwater optical images face various limitations that degrade the image quality compared with optical images taken in our atmosphere. Attenuation according to the wavelength of light and reflection by very small floating objects cause low contrast, blurry clarity, and color degradation in underwater images. We constructed an image data of the Korean sea and enhanced it by learning the characteristics of underwater images using the deep learning techniques of CycleGAN (cycle-consistent adversarial network), UGAN (underwater GAN), FUnIE-GAN (fast underwater image enhancement GAN). In addition, the underwater optical image was enhanced using the image processing technique of Image Fusion. For a quantitative performance comparison, UIQM (underwater image quality measure), which evaluates the performance of the enhancement in terms of colorfulness, sharpness, and contrast, and UCIQE (underwater color image quality evaluation), which evaluates the performance in terms of chroma, luminance, and saturation were calculated. For 100 underwater images taken in Korean seas, the average UIQMs of CycleGAN, UGAN, and FUnIE-GAN were 3.91, 3.42, and 2.66, respectively, and the average UCIQEs were measured to be 29.9, 26.77, and 22.88, respectively. The average UIQM and UCIQE of Image Fusion were 3.63 and 23.59, respectively. CycleGAN and UGAN qualitatively and quantitatively improved the image quality in various underwater environments, and FUnIE-GAN had performance differences depending on the underwater environment. Image Fusion showed good performance in terms of color correction and sharpness enhancement. It is expected that this method can be used for monitoring underwater works and the autonomous operation of unmanned vehicles by improving the visibility of underwater situations more accurately. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1225-0767 2287-6715 |
| Relation: | https://www.joet.org/journal/view.php?number=3044; https://doaj.org/toc/1225-0767; https://doaj.org/toc/2287-6715 |
| DOI: | 10.26748/KSOE.2021.095 |
| URL الوصول: | https://doaj.org/article/0cd88b6ba8674bb8aa5196c54938f239 |
| رقم الأكسشن: | edsdoj.0cd88b6ba8674bb8aa5196c54938f239 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 12250767 22876715 |
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| DOI: | 10.26748/KSOE.2021.095 |