TY - JOUR
T1 - Underwater image restoration and enhancement
T2 - a comprehensive review of recent trends, challenges, and applications
AU - Alsakar, Yasmin M.
AU - Sakr, Nehal A.
AU - El-Sappagh, Shaker
AU - Abuhmed, Tamer
AU - Elmogy, Mohammed
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
PY - 2025/4
Y1 - 2025/4
N2 - In recent years, underwater exploration for deep-sea resource utilization and development has a considerable interest. In an underwater environment, the obtained images and videos undergo several quality degradations resulting from light absorption and scattering, low contrast, color deviation, blurred details, and nonuniform illumination. Therefore, the restoration and enhancement of degraded images and videos are critical. Numerous techniques of image processing, pattern recognition, and computer vision have been proposed for image restoration and enhancement, but many challenges remain. This survey has been estimated to be superior to other reviews because it collects all their shortcomings and lacks and gives researchers many ideas for the future. This survey presents a comparison of the most prominent approaches in underwater image processing and analysis. It also discusses an overview of the underwater environment with a broad classification into enhancement and restoration techniques and introduces the main underwater image degradation reasons in addition to the underwater image model. The existing underwater image analysis techniques, methods, datasets, and evaluation metrics are presented in detail. Furthermore, the existing limitations are analyzed, which are classified into image-related and environment-related categories. In addition, the performance is validated on images from the UIEB dataset for qualitative, quantitative, and computational time assessment. Areas in which underwater images have recently been applied are briefly discussed. Finally, recommendations for future research are provided and the conclusion is presented.
AB - In recent years, underwater exploration for deep-sea resource utilization and development has a considerable interest. In an underwater environment, the obtained images and videos undergo several quality degradations resulting from light absorption and scattering, low contrast, color deviation, blurred details, and nonuniform illumination. Therefore, the restoration and enhancement of degraded images and videos are critical. Numerous techniques of image processing, pattern recognition, and computer vision have been proposed for image restoration and enhancement, but many challenges remain. This survey has been estimated to be superior to other reviews because it collects all their shortcomings and lacks and gives researchers many ideas for the future. This survey presents a comparison of the most prominent approaches in underwater image processing and analysis. It also discusses an overview of the underwater environment with a broad classification into enhancement and restoration techniques and introduces the main underwater image degradation reasons in addition to the underwater image model. The existing underwater image analysis techniques, methods, datasets, and evaluation metrics are presented in detail. Furthermore, the existing limitations are analyzed, which are classified into image-related and environment-related categories. In addition, the performance is validated on images from the UIEB dataset for qualitative, quantitative, and computational time assessment. Areas in which underwater images have recently been applied are briefly discussed. Finally, recommendations for future research are provided and the conclusion is presented.
KW - Underwater datasets
KW - Underwater image analysis
KW - Underwater image enhancement
KW - Underwater image quality evaluation
KW - Underwater image restoration
UR - https://www.scopus.com/pages/publications/105002947433
U2 - 10.1007/s00371-024-03630-w
DO - 10.1007/s00371-024-03630-w
M3 - Review article
AN - SCOPUS:105002947433
SN - 0178-2789
VL - 41
SP - 3735
EP - 3783
JO - Visual Computer
JF - Visual Computer
IS - 6
M1 - 116088
ER -