@article{4842,
  author = {Hsing-Cheng Liu, Yao-Liang Chung},
  title = {Synthetic Underwater Image Degradation: Classification, Restoration, Multi-Task Learning, Domain Adaptation, and Contrastive Representation Analysis},
  journal = {Journal of Multimedia Processing and Technologies},
  year = {2026},
  volume = {17},
  number = {4},
  doi = {https://doi.org/10.6025/jmpt/2026/17/4/185-207},
  url = {https://www.dline.info/jmpt/fulltext/v17n4/jmptv17n4_1.pdf},
  abstract = {Underwater image degradation severely limits the effectiveness of computer vision systems deployed on
autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). While underwater image
enhancement (UIE) is commonly used as a preprocessing step, improving perceptual visual quality does not
necessarily guarantee improved performance in downstream tasks, nor does a single restoration strategy
suffice for heterogeneous degradation types. This study presents a controlled, scene independent framework
to systematically evaluate underwater degradation, restoration, and representation learning. Using the
Synthetic Underwater Image Dataset (SUID) comprising 30 ground truth scenes and 900 synthetically
degraded variants across four categories (Bluish, Greenish, Hazy, and Low Light), we conducted experiments
in degradation classification, paired affine restoration, multi-task learning, and contrastive representation
analysis. To prevent data leakage, all evaluations utilized a strict scene independent partitioning strategy.
Results indicate that while chromatic and illumination degradations (Bluish, Greenish, Low Light) are
effectively mitigated by condition specific affine transformations (yielding up to +14.5 dB PSNR gain), physical
scattering (Hazy) resists simple affine correction. A shared multi task representation successfully balanced
degradation classification (Macro-F1: 0.780), quality prediction, and restoration (PSNR: 20.91 dB).
Furthermore, contrastive learning achieved a 94.4% same scene retrieval rate, demonstrating that
embeddings can preserve underlying scene identity while retaining degradation sensitivity. Ultimately, this
framework establishes a rigorous benchmark for degradation specific behavior and provides a reproducible
protocol for future synthetic to real domain adaptation in underwater vision.},
}