A Novel Underwater Image Enhancement Using Optimal Composite Backbone Network
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.
First ; Corresponding
Natural Science Foundation of Zhejiang Province[LGF20F020009];
|WOS Research Area|
Engineering ; Materials Science
Engineering, Multidisciplinary ; Materials Science, Biomaterials
|WOS Accession No|
Cited Times [WOS]:0
|Document Type||Journal Article|
|Department||Department of Mechanical and Energy Engineering|
1.Department of Mechanical and Energy Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Health Management System Engineering Center,School of Public Health,Hangzhou Normal University,Hangzhou,311121,China
3.Institute of Oceanographic Instrumentation,Qilu University of Technology (Shandong Academy of Sciences),Qingdao,266075,China
|First Author Affilication||Department of Mechanical and Energy Engineering|
|Corresponding Author Affilication||Department of Mechanical and Energy Engineering|
|First Author's First Affilication||Department of Mechanical and Energy Engineering|
Chen，Yuhan,Li，Qingfeng,Lu，Dongxin,et al. A Novel Underwater Image Enhancement Using Optimal Composite Backbone Network[J]. Biomimetics,2023,8(3).
Chen，Yuhan.,Li，Qingfeng.,Lu，Dongxin.,Kou，Lei.,Ke，Wende.,...&Wang，Zhen.(2023).A Novel Underwater Image Enhancement Using Optimal Composite Backbone Network.Biomimetics,8(3).
Chen，Yuhan,et al."A Novel Underwater Image Enhancement Using Optimal Composite Backbone Network".Biomimetics 8.3(2023).
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