Machine Learning for Underwater Hazy Image
Deep Learning Techniques
(Sprache: Englisch)
Image performance in underwater robots is one of the most challenging problems for autonomous underwater robotics due to light transmission in water. Although image restoration techniques can effectively remove a haze from a damaged image, they require...
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Image performance in underwater robots is one of the most challenging problems for autonomous underwater robotics due to light transmission in water. Although image restoration techniques can effectively remove a haze from a damaged image, they require multiple images from the same location making it difficult to use in real time. Considering the positive effects of in-depth learning strategies on other image processing problems such as coloring or finding objects, a deeper learning solution is proposed. The convolutional neural network is trained in image retrieval techniques to capture one image better than other image enhancement techniques. The proposed method is capable of producing high quality image restoration images with a single image as input. The neural network is verified using images from various locations and signals to prove the power of normal action.
Autoren-Porträt von Aditya Patel, Nidhi Singh, Ajay Soni
Patel, AdityaProf. Aditya Patel works as an Assistant Professor in CSE Dept. at LNCT Bhopal. Previously he worked as Web Designer & Developer in Ignatiuz S/W Pvt Lmtd, Indore. He has worked on more than 50 websites / softwares. He has 4 years teaching experience in Technical Colleges and Universities.
Bibliographische Angaben
- Autoren: Aditya Patel , Nidhi Singh , Ajay Soni
- 2021, 72 Seiten, Maße: 22 cm, Kartoniert (TB), Englisch
- Verlag: LAP Lambert Academic Publishing
- ISBN-10: 6203869473
- ISBN-13: 9786203869477
Sprache:
Englisch
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