摘要
Abstract
This paper aims to provide a systematic review of the current research status and development trends of underwater optical image quality assessment.Existing underwater image quality assessment(UIQA)approaches are reviewed from both subjective and objective perspectives.Their principles,advantages,limitations,and applicability are analyzed,with a focus on emerging deep learning-based techniques.The results indicate that while current methods have made certain progress in addressing complex degradation issues in underwater images such as light scattering,color distortion,and structural blur,they still face challenges including data scarcity and limited model generalization.In the future,it is necessary to enhance few-shot learning,multimodal fusion,and lightweight model design to improve the accuracy,robustness,and real-time performance of UIQA,thereby providing effective support for underwater image processing and marine observation applications.关键词
图像质量评价/水下光学图像/主观质量评估/客观质量评估Key words
image quality assessment/underwater optical images/subjective quality assessment/objective quality assessment分类
信息技术与安全科学
