Underwater Image High Definition Display Using the Multilayer Perceptron and Color Feature-Based SRCNN
High-definition display technology for underwater images is of great significance for many applications, such as marine animal observation, seabed mining, and marine fishery production. The traditional underwater visual display systems have problems, such as low visibility, poor real-time performanc...
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Published in | IEEE access Vol. 7; pp. 83721 - 83728 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
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2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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Abstract | High-definition display technology for underwater images is of great significance for many applications, such as marine animal observation, seabed mining, and marine fishery production. The traditional underwater visual display systems have problems, such as low visibility, poor real-time performance, and low resolution, and cannot meet the needs of real-time high-definition displays in extreme environments. To solve these issues, we propose an underwater image enhancement method and a corresponding image super-resolution algorithm. To improve the quality of underwater images, we modify the Retinex algorithm and combine it with a neural network. The Retinex algorithm is used to defog the underwater image, and then, the image brightness is improved by applying gamma correction. Then, by combining with the dark channel prior and multilayer perceptron, the transmission map is further refined to improve the dynamic range of the image. In the super-resolution process, the current convolutional neural network reconstruction algorithm is only trained on the Y channel, which will lead to problems due to the insufficient acquisition of the color feature. Therefore, an image super-resolution reconstruction algorithm that is based on color features is proposed. The experimental results show that the proposed method improves the reconstruction effect of the image edges and texture details, increases the image clarity, and enhances the image color recovery. |
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AbstractList | High-definition display technology for underwater images is of great significance for many applications, such as marine animal observation, seabed mining, and marine fishery production. The traditional underwater visual display systems have problems, such as low visibility, poor real-time performance, and low resolution, and cannot meet the needs of real-time high-definition displays in extreme environments. To solve these issues, we propose an underwater image enhancement method and a corresponding image super-resolution algorithm. To improve the quality of underwater images, we modify the Retinex algorithm and combine it with a neural network. The Retinex algorithm is used to defog the underwater image, and then, the image brightness is improved by applying gamma correction. Then, by combining with the dark channel prior and multilayer perceptron, the transmission map is further refined to improve the dynamic range of the image. In the super-resolution process, the current convolutional neural network reconstruction algorithm is only trained on the Y channel, which will lead to problems due to the insufficient acquisition of the color feature. Therefore, an image super-resolution reconstruction algorithm that is based on color features is proposed. The experimental results show that the proposed method improves the reconstruction effect of the image edges and texture details, increases the image clarity, and enhances the image color recovery. |
Author | Li, Yun Ma, Chunyan Li, Jianru Serikawa, Seiichi Ge, Zongyuan Zhang, Tingting Li, Yujie |
Author_xml | – sequence: 1 givenname: Yujie orcidid: 0000-0002-0275-2797 surname: Li fullname: Li, Yujie email: yzyjli@gmail.com organization: School of Information Engineering, Yangzhou University, Yangzhou, China – sequence: 2 givenname: Chunyan surname: Ma fullname: Ma, Chunyan organization: School of Information Engineering, Yangzhou University, Yangzhou, China – sequence: 3 givenname: Tingting surname: Zhang fullname: Zhang, Tingting organization: School of Information Engineering, Yangzhou University, Yangzhou, China – sequence: 4 givenname: Jianru surname: Li fullname: Li, Jianru email: lijianru@tongji.edu.cn organization: State Key Laboratory on Marine Geology, Tongji University, Shanghai, China – sequence: 5 givenname: Zongyuan surname: Ge fullname: Ge, Zongyuan organization: NVIDIA AI Technology Centre, Monash University, Melbourne, VIC, Australia – sequence: 6 givenname: Yun surname: Li fullname: Li, Yun organization: School of Information Engineering, Yangzhou University, Yangzhou, China – sequence: 7 givenname: Seiichi surname: Serikawa fullname: Serikawa, Seiichi organization: School of Engineering, Kyushu Institute of Technology, Kitakyushu, Japan |
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SubjectTerms | Algorithms Artificial neural networks Color Convolution Convolutional neural networks Display devices Extreme environments Feature extraction Fisheries High definition Image acquisition Image color analysis Image enhancement Image quality Image reconstruction Image resolution Low visibility Marine animals Multilayer perceptrons Neural networks Ocean floor Real time Retinex (algorithm) superresolution Training Underwater underwater imaging Visual observation |
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Title | Underwater Image High Definition Display Using the Multilayer Perceptron and Color Feature-Based SRCNN |
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