TY - GEN
T1 - A new database for evaluating underwater image processing methods
AU - Ma, Yupeng
AU - Feng, Xiaoyi
AU - Chao, Lujing
AU - Huang, Dong
AU - Xia, Zhaoqiang
AU - Jiang, Xiaoyue
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/10
Y1 - 2019/1/10
N2 - In this paper, we present a new, large-scale database on underwater image, which is called the NWPU underwater image database. This database contains 6240 underwater images of 40 objects. Each object is captured with 6 different levels of turbidity water, 4 lighting conditions and 6 different distances. Among them, we use the underwater images with turbidity value of 0 as Ground-truth. In addition, we captured the shadowless image of the object in the air and clear water. Different from other underwater databases, we capture underwater images with real high turbidity lake water instead of simulating the turbidity of water. This method ensures that the underwater images we captured are as close as possible to the real environment. We have given the database baseline which contains multi-scale Retinex with color restore (MSRCR) algorithms for enhancing images and four commonly used image quality evaluation criteria, including two full-references and two no-references methods. The four image quality evaluation methods include two no-reference and two full reference.
AB - In this paper, we present a new, large-scale database on underwater image, which is called the NWPU underwater image database. This database contains 6240 underwater images of 40 objects. Each object is captured with 6 different levels of turbidity water, 4 lighting conditions and 6 different distances. Among them, we use the underwater images with turbidity value of 0 as Ground-truth. In addition, we captured the shadowless image of the object in the air and clear water. Different from other underwater databases, we capture underwater images with real high turbidity lake water instead of simulating the turbidity of water. This method ensures that the underwater images we captured are as close as possible to the real environment. We have given the database baseline which contains multi-scale Retinex with color restore (MSRCR) algorithms for enhancing images and four commonly used image quality evaluation criteria, including two full-references and two no-references methods. The four image quality evaluation methods include two no-reference and two full reference.
KW - image enhancement and restoration
KW - image quality evaluation
KW - turbidity
KW - underwater image
UR - http://www.scopus.com/inward/record.url?scp=85061908890&partnerID=8YFLogxK
U2 - 10.1109/IPTA.2018.8608131
DO - 10.1109/IPTA.2018.8608131
M3 - 会议稿件
AN - SCOPUS:85061908890
T3 - 2018 8th International Conference on Image Processing Theory, Tools and Applications, IPTA 2018 - Proceedings
BT - 2018 8th International Conference on Image Processing Theory, Tools and Applications, IPTA 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Image Processing Theory, Tools and Applications, IPTA 2018
Y2 - 7 November 2018 through 10 November 2018
ER -