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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.
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Machine Learning for Underwater Hazy Image
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LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203869473
ISBN 13: 9786203869477
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: 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. Bestandsnummer des Verkäufers 488320856
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Machine Learning for Underwater Hazy Image
Verlag:
LAP LAMBERT Academic Publishing Jun 2021, 2021
ISBN 10: 6203869473
ISBN 13: 9786203869477
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Taschenbuch. Zustand: Neu. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 72 pp. Englisch. Bestandsnummer des Verkäufers 9786203869477
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Machine Learning for Underwater Hazy Image
Verlag:
LAP LAMBERT Academic Publishing Jun 2021, 2021
ISBN 10: 6203869473
ISBN 13: 9786203869477
Neu
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Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 72 pp. Englisch. Bestandsnummer des Verkäufers 9786203869477
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Machine Learning for Underwater Hazy Image : Deep Learning Techniques
Verlag:
LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203869473
ISBN 13: 9786203869477
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Bestandsnummer des Verkäufers 9786203869477
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Machine Learning for Underwater Hazy Image: Deep Learning Techniques
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LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203869473
ISBN 13: 9786203869477
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Machine Learning for Underwater Hazy Image: Deep Learning Techniques
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LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203869473
ISBN 13: 9786203869477
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Machine Learning for Underwater Hazy Image: Deep Learning Techniques
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LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203869473
ISBN 13: 9786203869477
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