Learning-Based Dark and Blurred Underwater Image Restoration
Joint Authors
Xu, Yifeng
Wang, Huigang
Cooper, Garth Douglas
Rong, Shaowei
Sun, Weitao
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-01
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Underwater image processing is a difficult subtopic in the field of computer vision due to the complex underwater environment.
Since the light is absorbed and scattered, underwater images have many distortions such as underexposure, blurriness, and color cast.
The poor quality hinders subsequent processing such as image classification, object detection, or segmentation.
In this paper, we propose a method to collect underwater image pairs by placing two tanks in front of the camera.
Due to the high-quality training data, the proposed restoration algorithm based on deep learning achieves inspiring results for underwater images taken in a low-light environment.
The proposed method solves two of the most challenging problems for underwater image: darkness and fuzziness.
The experimental results show that the proposed method surpasses most other methods.
American Psychological Association (APA)
Xu, Yifeng& Wang, Huigang& Cooper, Garth Douglas& Rong, Shaowei& Sun, Weitao. 2020. Learning-Based Dark and Blurred Underwater Image Restoration. Complexity،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1142962
Modern Language Association (MLA)
Xu, Yifeng…[et al.]. Learning-Based Dark and Blurred Underwater Image Restoration. Complexity No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1142962
American Medical Association (AMA)
Xu, Yifeng& Wang, Huigang& Cooper, Garth Douglas& Rong, Shaowei& Sun, Weitao. Learning-Based Dark and Blurred Underwater Image Restoration. Complexity. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1142962
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142962