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- Title
A Novel Underwater Image Enhancement Using Optimal Composite Backbone Network.
- Authors
Chen, Yuhan; Li, Qingfeng; Lu, Dongxin; Kou, Lei; Ke, Wende; Bai, Yan; Wang, Zhen
- Abstract
Continuous exploration of the ocean has made underwater image processing an important research field, and plenty of CNN (convolutional neural network)-based underwater image enhancement methods have emerged over time. However, the feature-learning ability of existing CNN-based underwater image enhancement is limited. The networks were designed to be complicated or embed other algorithms for better results, which cannot simultaneously meet the requirements of suitable underwater image enhancement effects and real-time performance. Although the composite backbone network (CBNet) was introduced in underwater image enhancement, we proposed OECBNet (optimal underwater image-enhancing composite backbone network) to obtain a better enhancement effect and shorten the running time. Herein, a comprehensive study of different composite architectures in an underwater image enhancement network was carried out by comparing the number of backbones, connection strategies, pruning strategies for composite backbones, and auxiliary losses. Then, a CBNet with optimal performance was obtained. Finally, cross-sectional research of the obtained network with the state-of-the-art underwater enhancement network was performed. The experiments showed that our optimized composite backbone network achieved better-enhanced images than those of existing CNN-based methods.
- Subjects
CONVOLUTIONAL neural networks; UNDERWATER imaging systems; DEEP learning; COMPOSITE materials; MACHINE learning; ARTIFICIAL neural networks
- Publication
Biomimetics (2313-7673), 2023, Vol 8, Issue 3, p275
- ISSN
2313-7673
- Publication type
Article
- DOI
10.3390/biomimetics8030275