Super-Resolution Reconstruction of Underwater Image Based on Image Sequence Generative Adversarial Network
Joint Authors
Wang, Zhiqiong
Li, Li
Fan, Zijia
Zhao, Mingyang
Wang, Xinlei
Wang, Zhongyang
Guo, Longxiang
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-28
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Since the underwater image is not clear and difficult to recognize, it is necessary to obtain a clear image with the super-resolution (SR) method to further study underwater images.
The obtained images with conventional underwater image super-resolution methods lack detailed information, which results in errors in subsequent recognition and other processes.
Therefore, we propose an image sequence generative adversarial network (ISGAN) method for super-resolution based on underwater image sequences collected by multifocus from the same angle, which can obtain more details and improve the resolution of the image.
At the same time, a dual generator method is used in order to optimize the network architecture and improve the stability of the generator.
The preprocessed images are, respectively, passed through the dual generator, one of which is used as the main generator to generate the SR image of sequence images, and the other is used as the auxiliary generator to prevent the training from crashing or generating redundant details.
Experimental results show that the proposed method can be improved on both peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared to the traditional GAN method in underwater image SR.
American Psychological Association (APA)
Li, Li& Fan, Zijia& Zhao, Mingyang& Wang, Xinlei& Wang, Zhongyang& Wang, Zhiqiong…[et al.]. 2020. Super-Resolution Reconstruction of Underwater Image Based on Image Sequence Generative Adversarial Network. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1201133
Modern Language Association (MLA)
Li, Li…[et al.]. Super-Resolution Reconstruction of Underwater Image Based on Image Sequence Generative Adversarial Network. Mathematical Problems in Engineering No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1201133
American Medical Association (AMA)
Li, Li& Fan, Zijia& Zhao, Mingyang& Wang, Xinlei& Wang, Zhongyang& Wang, Zhiqiong…[et al.]. Super-Resolution Reconstruction of Underwater Image Based on Image Sequence Generative Adversarial Network. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1201133
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1201133