计算机科学
水准点(测量)
水下
公制(单位)
图像质量
人工智能
数据挖掘
模式识别(心理学)
机器学习
图像(数学)
工程类
地图学
运营管理
海洋学
地质学
地理
作者
Qiuping Jiang,Yuese Gu,Chongyi Li,Runmin Cong,Feng Shao
出处
期刊:IEEE Transactions on Circuits and Systems for Video Technology
[Institute of Electrical and Electronics Engineers]
日期:2022-04-05
卷期号:32 (9): 5959-5974
被引量:133
标识
DOI:10.1109/tcsvt.2022.3164918
摘要
Due to the attenuation and scattering of light by water, there are many quality defects in raw underwater images such as color casts, decreased visibility, reduced contrast, et al. . Many different underwater image enhancement (UIE) algorithms have been proposed to enhance underwater image quality. However, how to fairly compare the performance among UIE algorithms remains a challenging problem. So far, the lack of comprehensive human subjective user study with large-scale benchmark dataset and reliable objective image quality assessment (IQA) metric makes it difficult to fully understand the true performance of UIE algorithms. We in this paper make efforts in both subjective and objective aspects to fill these gaps. Firstly, we construct a new Subjectively-Annotated UIE benchmark Dataset (SAUD) which simultaneously provides real-world raw underwater images, readily available enhanced results by representative UIE algorithms, and subjective ranking scores of each enhanced result. Secondly, we propose an effective No-reference (NR) Underwater Image Quality metric (NUIQ) to automatically evaluate the visual quality of enhanced underwater images. Experiments on the constructed SAUD dataset demonstrate the superiority of our proposed NUIQ metric, achieving higher consistency with subjective rankings than 22 mainstream NR-IQA metrics. The dataset and source code will be made available at https://github.com/yia-yuese/SAUD-Dataset .
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