1 |
S. Zhang and T. Wang et al., "Underwater image enhancement via extended multi-scale retinex," Neurocomputing, vol. 245, pp. 1-9, 2017.
DOI
|
2 |
C. Li, S. Anwar, and F. Porikli, "Underwater scene prior inspired deep underwater image and video enhancement," Pattern Recogn, vol. 98, 2020.
|
3 |
S. Anwar, C. Li, and F. Porikli, "Deep Underwater Image Enhancement," ArXiv180703528, Jul. 2018.
|
4 |
X. Chen and J. Yu et al., "Towards real-time advancement of underwater visual quality with gan," IEEE Transactions on Ind. Electron, vol. 66, no. 12, pp. 9350 - 9359, 2019.
DOI
|
5 |
H. Dong and J. Pan et al., "Multi-scale boosted dehazing network with dense feature fusion," in Proc. of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2157-2167, 2020.
|
6 |
Y. Romano and M. Elad, "Boosting of image denoising algorithms," SIAM Journal on Imaging Sciences, vol. 8, no. 2, pp. 1187-1219, Jan. 2015.
DOI
|
7 |
L. Chen, Q. S. Sun, and F. Wang, "Attention-adaptive and deformable convolutional modules for dynamic scenedeblurring," Information Sciences, vol. 546, pp. 368-377, 2021.
DOI
|
8 |
B. Cai, X. Xu, and K. Guo, "A joint intrinsic extrinsic prior model for retinex," in Proc. of 2017 IEEE Int. Conf. on Comput. Vis. (ICCV), pp. 4000-4009, 2017.
|
9 |
X. Fu and P. Zhuang et al., "A retinex-based enhancing approach for single underwater image," in Proc. of 2014 IEEE International Conference on Image Processing (ICIP), pp. 4572-4576, 2014.
|
10 |
C. Fabbri, M. Islam, and J. Sattar, "Enhancing underwater imagery using generative adversarial networks," in Proc. of 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 7159-7165, 2018.
|
11 |
C., Li, C. Guo, W. Ren, R. Cong, et al., "An underwater image enhancement benchmark dataset and beyond," IEEE Transactions on Image Processing, vol. 29, pp. 4376-4389, 2020.
DOI
|
12 |
X. Liu, Z. Gao, and B. M. Chen, "IPMGAN: integrating physical model and gener- ative adversarial network forunderwater image enhancement," Neurocomputing, vol. 453, pp. 538-551, 2021.
DOI
|
13 |
R. Liu, X. Fan, M. Zhu, M. Hou and Z. Luo, "Real-World Underwater Enhancement: Challenges, Benchmarks, and Solutions Under Natural Light," IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 12, pp. 4861-4875, Dec. 2020.
DOI
|
14 |
Z. Liang, Y. Wang, X. Ding, Z. Mi, and X. Fu, "Single underwater image enhancement by attenuation map guided color correction and detail preserved dehazing," Neurocomputing, vol. 425, no. 15, pp. 160-172, 2021.
DOI
|
15 |
J. Xu, Y. Hou, and D. Ren et al., "Star: A structure and texture aware retinex model," IEEE Transactions on ImageProcess, vol. 29, pp. 5022-5037, 2020.
DOI
|
16 |
C. Ancuti, C. O. Ancuti, T. Haber, P. Bekaert, "Enhancing underwater images and videos by fusion," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 81-88, 2012.
|
17 |
K. He, S. Jian, and X. Tang, "Single image haze removal using dark channel prior," Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 12, pp. 2341-2353, Dec. 2011.
DOI
|
18 |
M. J. Islam and Y. Xia et al., "Fast underwater image enhancement for improved visual perception," IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 3227-3234, April 2020.
DOI
|
19 |
M. Li, J. Liu, and W. Yang et al., "Structure-revealing low-light image enhancement via robust retinex model," IEEE Transactions on Image Processing, vol. 27, no. 6, pp. 2828-2841, June 2018.
DOI
|
20 |
L. Chao and M. Wang, "Removal of water scattering," in Proc. of 2010 2nd International Conference on Computer Engineering and Technology, vol. 2, pp. V2-35-V2-39, 2010.
|
21 |
Y. Zhou, K. Yan and X. Li, "Underwater Image Enhancement via Physical-Feedback Adversarial Transfer Learning," IEEE Journal of Oceanic Engineering, vol. 47, no. 1, pp. 76-87, Jan. 2022.
DOI
|
22 |
K. Zuiderveld, "Contrast limited adaptive histogram equalization," Graph. Gems, vol. 38, pp. 474-485, 1994.
DOI
|
23 |
X. Fu and X. Cao, "Underwater image enhancement with global-local networks and compressed-histogram equalization," Signal Processing: Image Communication, vol. 86, 2020.
|
24 |
D. Berman, D. Levy, and S. Avidan et al., "Underwater single image color restoration using haze-lines and a new quantitative dataset," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 8, pp. 2822-2837, 1 Aug. 2021.
|
25 |
K. Panetta, C. Gao and S. Agaian, "Human-Visual-System-Inspired Underwater Image Quality Measures," IEEE Journal of Oceanic Engineering, vol. 41, no. 3, pp. 541-551, July 2016.
DOI
|
26 |
M. Yang and A. Sowmya, "An Underwater Color Image Quality Evaluation Metric," IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 6062-6071, Dec. 2015.
DOI
|
27 |
D. Chen and M. He et al., "Gated context aggregation network for image dehazing and deraining," in Proc. of 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1375-1383, 2019.
|
28 |
W. Song, Y. Wang, D. Huang, A. Liotta, and C. Perra, "Enhancement of underwater images with statistical model of background light and optimization of transmission map," IEEE Transactions on Broadcasting, vol. 66, no. 1, pp. 153-169, March 2020.
DOI
|
29 |
J. Zhou, T. Yang, W. Ren, D. Zhang, and W. Zhang, "Underwater image restoration via depth map and illumination estimation based on a single image," Opt. Express, vol. 29, no. 18, pp. 29864-29886, 2021.
DOI
|
30 |
R. Hummel, "Image enhancement by histogram transformation," Comput. Graph. Image Process., pp. 184-195, 1975.
|