Single Image-based Enhancement Techniques for Underwater Optical Imaging KCI

Title
Single Image-based Enhancement Techniques for Underwater Optical Imaging
Author(s)
Kim, Do Gyun; Kim, Soo Mee
KIOST Author(s)
Kim, Soo Mee(김수미)
Alternative Author(s)
김수미
Publication Year
2020-11
Abstract
Underwater color images suffer from low visibility and color cast effects caused by light attenuation by water and floating particles. This study applied single image enhancement techniques to enhance the quality of underwater images and compared their performance with real underwater images taken in Korean waters. Dark channel prior (DCP), gradient transform, image fusion, and generative adversarial networks (GAN), such as cycleGAN and underwater GAN (UGAN), were considered for single image enhancement. Their performance was evaluated in terms of underwater image quality measure, underwater color image quality evaluation, gray-world assumption, and blur metric. The DCP saturated the underwater images to a specific greenish or bluish color tone and reduced the brightness of the backgroundsignal. The gradient transform method with two transmission maps were sensitive to the light source and highlighted the region exposed to light. Although image fusion enabled reasonable color correction, the object details were lost due to the last fusion step. CycleGAN corrected overall color tone relatively well but generated artifacts in the background. UGAN showed good visual quality and obtained the highest scores against all figures of merit (FOMs) by compensating for the colors and visibility compared to the other single enhancement methods.
ISSN
1225-0767
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/38973
DOI
10.26748/KSOE.2020.030
Bibliographic Citation
Journal of Ocean Engineering and Technology, v.34, no.6, pp.442 - 453, 2020
Publisher
한국해양공학회
Keywords
Dark channel prior; Generative adversarial network; Gradient transform enhancement; Image fusion enhancement; Underwater color image; Single image enhancement
Type
Article
Language
English
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