An Underwater Image Quality Assessment Metric

被引:13
作者
Guo, Pengfei [1 ,2 ]
Liu, Hantao [3 ]
Zeng, Delu [2 ]
Xiang, Tao [4 ]
Li, Leida [5 ]
Gu, Ke [6 ]
机构
[1] Zhongkai Univ Agr & Engn, Sch Computat Sci, Guangzhou 510225, Peoples R China
[2] South China Univ Technol, Sch Math, Guangzhou 510641, Peoples R China
[3] Cardiff Univ, Sch Comp Sci & Informat, Cardiff CF24 3AA, Wales
[4] Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China
[5] Xidian Univ, Sch Artificial Intelligence, Xian 710071, Peoples R China
[6] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
关键词
Underwater; image quality; enhancement; human visual system; objective metric; VISUAL-ATTENTION; ENHANCEMENT; FRAMEWORK; VISIBILITY; VISION; LIGHT; COLOR;
D O I
10.1109/TMM.2022.3187212
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Various image enhancement algorithms are adopted to improve underwater images that often suffer from visual distortions. It is critical to assess the output quality of underwater images undergoing enhancement algorithms, and use the results to optimise underwater imaging systems. In our previous study, we created a benchmark for quality assessment of underwater image enhancement via subjective experiments. Building on the benchmark, this paper proposes a new objective metric that can automatically assess the output quality of image enhancement, namely UWEQM. By characterising specific underwater physics and relevant properties of the human visual system, image quality attributes are computed and combined to yield an overall metric. Experimental results show that the proposed UWEQM metric yields good performance in predicting image quality as perceived by human subjects.
引用
收藏
页码:5093 / 5106
页数:14
相关论文
共 82 条
[1]   A Revised Underwater Image Formation Model [J].
Akkaynak, Derya ;
Treibitz, Tali .
2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2018, :6723-6732
[2]   A Robust, Adaptive, Solar-Powered WSN Framework for Aquatic Environmental Monitoring [J].
Alippi, Cesare ;
Camplani, Romolo ;
Galperti, Cristian ;
Roveri, Manuel .
IEEE SENSORS JOURNAL, 2011, 11 (01) :45-55
[3]   Single Image Dehazing by Multi-Scale Fusion [J].
Ancuti, Codruta Orniana ;
Ancuti, Cosmin .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2013, 22 (08) :3271-3282
[4]   Archaeology of the continental shelf: Marine resources, submerged landscapes and underwater archaeology [J].
Bailey, Geoffrey N. ;
Flemming, Nicholas C. .
QUATERNARY SCIENCE REVIEWS, 2008, 27 (23-24) :2153-2165
[5]   Non-Local Image Dehazing [J].
Berman, Dana ;
Treibitz, Tali ;
Avidan, Shai .
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :1674-1682
[6]   Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment [J].
Bosse, Sebastian ;
Maniry, Dominique ;
Mueller, Klaus-Robert ;
Wiegand, Thomas ;
Samek, Wojciech .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2018, 27 (01) :206-219
[7]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[8]  
Carlevaris-Bianco N, 2010, OCEANS-IEEE
[9]   A Local Contrast Method for Small Infrared Target Detection [J].
Chen, C. L. Philip ;
Li, Hong ;
Wei, Yantao ;
Xia, Tian ;
Tang, Yuan Yan .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2014, 52 (01) :574-581
[10]   No-Reference Image Quality Assessment: An Attention Driven Approach [J].
Chen, Diqi ;
Wang, Yizhou ;
Gao, Wen .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 29 :6496-6506