Self-learning Methodology Based on Degradation Estimation for Underwater Image Enhancement-Reference-Cited by-同舟云学术

Self-learning Methodology Based on Degradation Estimation for Underwater Image Enhancement

Published:2022 Issue: Volume: Page:84-95
ISSN:0302-9743
Container-title:Intelligent Systems
language:
Short-container-title:

Author:

Mello Claudio Dornelles^ORCID,Moreira Bryan Umpierre^ORCID,de Oliveira Evald Paulo Jefferson Dias^ORCID,Drews Paulo Jorge Lilles^ORCID,Botelho Silvia Silva Costa^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-21689-3_7

Reference21 articles.

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2. Berman, D., Levy, D., Avidan, S., Treibitz, T.: Underwater single image color restoration using haze-lines and a new quantitative dataset. IEEE Trans. Pattern Anal. Mach. Intell. 43(8), 2822–2837 (2021). https://doi.org/10.1109/TPAMI.2020.2977624

3. Burger, W., Burge, M.J.: Digital Image Processing: An Algorithmic Introduction Using Java, 2nd edn. Springer, London (2016). https://doi.org/10.1007/978-1-4471-6684-9

4. Cho, Y., Jang, H., Malav, R., Pandey, G., Kim, A.: Underwater Image Dehazing via Unpaired Image-to-image Translation. Int. J. Control Autom. Syst. 18(3), 605–614 (2020). https://doi.org/10.1007/s12555-019-0689-x

5. Donaldson, J.A., Drews-Jr, P., Bradley, M., Morgan, D.L., Baker, R., Ebner, B.C.: Countering low visibility in video survey of an estuarine fish assemblage. Pac. Conserv. Biol. 26(2), 190–200 (2020)

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