Underwater Image Enhancement with a Deep Residual Framework

Authors

  • Anuja Phapale  Professor, BE Information Technology, A.I.S.S.M.S's Institute of Information Technology, Pune, India
  • Puja Kasture  Student, BE Information Technology, A.I.S.S.M.S's Institute of Information Technology, Pune, India
  • Keshav Katkar  
  • Omkar Karale  
  • Atal Deshmukh  

DOI:

https://doi.org/10.32628/IJSRST218428

Keywords:

Asynchronous Training, Edge Difference Loss, Residual Learning, Underwater Image Enhancement.

Abstract

This paper focuses on framework developed with the goal to enhance the quality of underwater images using machine learning models for the Underwater Image enhancement system. It also covers the various technologies and language used in the development process using Python programming language. The developed system provides two major functionality such as feature to provide input as image or video and returns enhanced image or video depending upon user input type with focus on more image quality, sharpness, colour correctness etc.

References

  1. P.Liu, "Underwater Image enhancement using deep residual framework," 2019.
  2. H.Qi, "Underwater Image enhancement using deep residual framework," 2019.
  3. C.Zhenge, "Underwater Image enhancement using deep residual framework," 2019.
  4. Z.Yu, "Underwater Image enhancement using deep residual framework," 2019.
  5. P. Luo, "Underwater Image super-resolution using deep residual multipliers," 2019.
  6. Y. Wang, "A deep CNN Methood for the Underwater image enhancement," 2017.
  7. J. Sattar, "Underwater image super-resolution with deep residual multipliers," 2019.
  8. H.Lu, "Underwater Image super-resolution by descattering and fusion," 2017.
  9. R.Hummel, "Image enhancement by histogram transformation,Compute Graph,Image Process," 2017.
  10. S.Corchs, "Underwater Image processing. State of the art of restoration and image enhancement methods," 2014.

International Journal of Scientific Research in Science and Technology

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Published

2021-08-30

Issue

Volume 8, Issue 4, July-August-2021

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Anuja Phapale, Puja Kasture, Keshav Katkar, Omkar Karale, Atal Deshmukh "Underwater Image Enhancement with a Deep Residual Framework" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 8, Issue 4, pp.200-206, July-August-2021. Available at doi : https://doi.org/10.32628/IJSRST218428