Open Access
ITM Web Conf.
Volume 44, 2022
International Conference on Automation, Computing and Communication 2022 (ICACC-2022)
Article Number 03066
Number of page(s) 6
Section Computing
Published online 05 May 2022
  1. Hashisho, Yousif, Mohamad Albadawi, Tom Krause, and Uwe Freiherr von Lukas. “Underwater color restoration using u-net denoising autoencoder.” In 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 117–122. IEEE, 2019. [CrossRef] [Google Scholar]
  2. Irfan, Muhammad, Jiangbin Zheng, Muhammad Iqbal, and Muhammad Hassan Arif. “A novel feature extraction model to enhance underwater image classification.” In International Symposium on Intelligent Computing Systems, pp. 78–91. Springer, Cham, 2020. [CrossRef] [Google Scholar]
  3. Sun, Xin, Junyu Shi, Lipeng Liu, Junyu Dong, Claudia Plant, Xinhua Wang, and Huiyu Zhou. “Transferring deep knowledge for object recognition in low-quality underwater videos.” Neurocomputing 275 (2018): 897–908. [CrossRef] [Google Scholar]
  4. Honnutagi, Pooja, V. D. Mytri, and Y. S. Lalitha. “Fusion-based underwater image enhancement by weight map techniques.” In Recent Developments in Machine Learning and Data Analytics, pp. 327–339. Springer, Singapore, 2019. [CrossRef] [Google Scholar]
  5. Liu, Risheng, Xin Fan, Ming Zhu, Minjun Hou, and Zhongxuan Luo. “Real-world underwater enhancement: challenges, benchmarks, and solutions.” arXiv preprint arXiv:1901.05320 (2019). [Google Scholar]
  6. Liu, Risheng, Xin Fan, Ming Zhu, Minjun Hou, and Zhongxuan Luo. “Real-world underwater enhancement: challenges, benchmarks, and solutions.” arXiv preprint arXiv:1901.05320 (2019). [Google Scholar]
  7. Tang, Chong, Uwe Freiherr von Lukas, Matthias Vahl, Shuo Wang, Yu Wang, and Min Tan. “Efficient underwater image and video enhancement based on Retinex.” Signal, Image and Video Processing 13, no. 5 (2019): 1011–1018. [CrossRef] [Google Scholar]
  8. Yadav, Anushka, Mayank Upadhyay, and Ghanapriya Singh. “Underwater Image Enhancement Using Convolutional Neural Network.” arXiv preprint arXiv:2109.08916 (2021). [Google Scholar]
  9. Wang, Xingmei, Yixu Zhao, Xuyang Teng, and Weiqi Sun. “A stacked convolutional sparse denoising autoencoder model for underwater heterogeneous information data.” Applied Acoustics 167 (2020): 107391. [CrossRef] [Google Scholar]
  10. Mello, Claudio D., Paulo L. Drews, and Silvia C. Botelho. “Degradation-Driven Underwater Image Enhancement.” In 2021 Latin American Robotics Symposium (LARS), 2021 Brazilian Symposium on Robotics (SBR), and 2021 Workshop on Robotics in Education (WRE), pp. 186–191. IEEE, 2021. [CrossRef] [Google Scholar]
  11. M. J. Islam, Y. Xia and J. Sattar, “Fast Underwater Image Enhancement for Improved Visual Perception,” in IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 3227–3234, April 2020, doi: 10.1109/LRA.2020.2974710. [CrossRef] [Google Scholar]
  12. Liou, Cheng-Yuan, Wei-Chen Cheng, Jiun-Wei Liou, and Daw-Ran Liou. “Autoencoder for words.” Neurocomputing 139 (2014): 84–96. [CrossRef] [Google Scholar]
  13. Tschannen, Michael, Olivier Bachem, and Mario Lucic. “Recent advances in autoencoder-based representation learning.” arXiv preprint arXiv:1812.05069 (2018). [Google Scholar]
  14. Wang, Yasi, Hongxun Yao, and Sicheng Zhao. “Auto-encoder based dimensionality reduction.” Neurocomputing 184 (2016): 232–242. [CrossRef] [Google Scholar]
  15. Badrinarayanan, Vijay, Alex Kendall, and Roberto Cipolla. “Segnet: A deep convolutional encoderdecoder architecture for image segmentation.” IEEE transactions on pattern analysis and machine intelligence 39, no. 12 (2017): 2481–2495. [CrossRef] [Google Scholar]
  16. Agarap, Abien Fred. “Deep learning using rectified linear units (relu).” arXiv preprint arXiv:1803.08375 (2018). [Google Scholar]
  17. Qin, Hongwei, Xiu Li, Zhixiong Yang, and Min Shang. “When underwater imagery analysis meets deep learning: A solution at the age of big visual data.” In OCEANS 2015-MTS/IEEE Washington, pp. 1–5. IEEE, 2015. [Google Scholar]
  18. Irsoy, Ozan, and Ethem Alpaydin. “Autoencoder trees.” In Asian conference on machine learning, pp. 378–390. PMLR, 2016. [Google Scholar]
  19. Yang, Miao, and Arcot Sowmya. “An underwater color image quality evaluation metric.” IEEE Transactions on Image Processing 24, no. 12 (2015): 6062–6071. [CrossRef] [MathSciNet] [Google Scholar]
  20. C. Li et al., “An Underwater Image Enhancement Benchmark Dataset and Beyond,” in IEEE Transactions on Image Processing, vol. 29, pp. 4376–4389, 2020, doi: 10.1109/TIP.2019.2955241. [CrossRef] [Google Scholar]
  21. Panetta, Karen, Chen Gao, and Sos Agaian. “Humanvisual-system-inspired underwater image quality measures.” IEEE Journal of Oceanic Engineering 41, no. 3 (2015): 541–551. [Google Scholar]
  22. Raveendran, Smitha, Mukesh D. Patil, and Gajanan K. Birajdar. “Underwater image enhancement: a comprehensive review, recent trends, challenges and applications.” Artificial Intelligence Review 54, no. 7 (2021): 5413–5467. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.