TY - GEN
T1 - Towards Robust Underwater Image Enhancement
AU - Marvi, Jahroo Nabila
AU - Rahadianti, Laksmita
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - Underwater images often suffer from blurring and color distortion due to absorption and scattering in the water. Such effects are undesirable since they may hinder computer vision tasks. Many underwater image enhancement techniques have been explored to address this issue, each to varying degrees of success. The large variety of distortions in underwater images is difficult to handle by any singular method. This study observes four underwater image enhancement methods, i.e., Underwater Light Attenuation Prior (ULAP), statistical Background Light and Transmission Map estimation (BLTM), and Natural-based Underwater Image Color Enhancement (NUCE), and Global–Local Networks (GL-Net). These methods are evaluated on the Underwater Image Enhancement Benchmark (UIEB) dataset using quantitative metrics, e.g., SSIM, PSNR, and CIEDE2000 as the metrics. Additionally, a qualitative analysis of image quality attributes is also performed. The results show that GL-Net achieves the best enhancement result, but based on the qualitative assessment, this method still has room for improvement. A proper combination between the non-learning-based component and learning-based component should be investigated to further improve the robustness of the method.
AB - Underwater images often suffer from blurring and color distortion due to absorption and scattering in the water. Such effects are undesirable since they may hinder computer vision tasks. Many underwater image enhancement techniques have been explored to address this issue, each to varying degrees of success. The large variety of distortions in underwater images is difficult to handle by any singular method. This study observes four underwater image enhancement methods, i.e., Underwater Light Attenuation Prior (ULAP), statistical Background Light and Transmission Map estimation (BLTM), and Natural-based Underwater Image Color Enhancement (NUCE), and Global–Local Networks (GL-Net). These methods are evaluated on the Underwater Image Enhancement Benchmark (UIEB) dataset using quantitative metrics, e.g., SSIM, PSNR, and CIEDE2000 as the metrics. Additionally, a qualitative analysis of image quality attributes is also performed. The results show that GL-Net achieves the best enhancement result, but based on the qualitative assessment, this method still has room for improvement. A proper combination between the non-learning-based component and learning-based component should be investigated to further improve the robustness of the method.
KW - Deep learning
KW - Image enhancement
KW - Image restoration
KW - Underwater images
UR - http://www.scopus.com/inward/record.url?scp=85151122845&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-0405-1_15
DO - 10.1007/978-981-99-0405-1_15
M3 - Conference contribution
AN - SCOPUS:85151122845
SN - 9789819904044
T3 - Communications in Computer and Information Science
SP - 211
EP - 221
BT - Soft Computing in Data Science - 7th International Conference, SCDS 2023, Proceedings
A2 - Yusoff, Marina
A2 - Kassim, Murizah
A2 - Mohamed, Azlinah
A2 - Hai, Tao
A2 - Kita, Eisuke
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th International Conference on Soft Computing in Data Science, SCDS 2023
Y2 - 24 January 2023 through 25 January 2023
ER -