한국해양공학회지 (Journal of Ocean Engineering and Technology)

  • 제36권1호
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  • Pages.32-40
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  • 2022
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  • 1225-0767(pISSN)
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  • 2287-6715(eISSN)
한국해양공학회 (Korean Society of Ocean Engineers)
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Comparison of GAN Deep Learning Methods for Underwater Optical Image Enhancement

  • Kim, Hong-Gi (Department. of Convergence Study on the Ocean Science and Technology, Korea Maritime and Ocean University) ;
  • Seo, Jung-Min (Maritime ICT R&D Center, Korea Institute of Ocean Science and Technology) ;
  • Kim, Soo Mee (Maritime ICT R&D Center, Korea Institute of Ocean Science and Technology)
  • 투고 : 2021.12.06
  • 심사 : 2021.12.30
  • 발행 : 2022.02.28
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초록

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.

키워드

과제정보

This study was conducted with the support of the National Research Foundation of Korea (Development of Underwater Stereo Camera Stereoscopic Visualization Technology, NRF-2021R1A2C2006682) with funding from the Ministry of Science and ICT in 2021 and the Korea Institute of Marine Science & Technology Promotion (establishment of test evaluation ships and systems for the verification of the marine equipment performance in real sea areas) with funding from the Ministry of Oceans and Fisheries in 2021.

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