9786203869477 - Machine Learning for Underwater Hazy Image: Deep Learning Techniques de Patel, Aditya; Singh, Nidhi; Soni, Ajay (5 resultados)

Taschenbuch. Condición: Neu. Machine Learning for Underwater Hazy Image | Deep Learning Techniques | Aditya Patel (u. a.) | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786203869477 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condición: 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.

Condición: 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.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 72 pp. Englisch.

Taschenbuch. Condición: 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.