A Novel Underwater Image Enhancement Using Optimal Composite Backbone Network

Chen，Yuhan1; Li，Qingfeng2; Lu，Dongxin2; Kou，Lei3; Ke，Wende1; Bai，Yan1; Wang，Zhen1

2023-09-01

10.3390/biomimetics8030275

Biomimetics   Impact Factor & Quartile

2313-7673

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.

composite architectures composite backbone deep learning underwater image enhancement

SCI

English

First ; Corresponding

Natural Science Foundation of Zhejiang Province[LGF20F020009];

Engineering ; Materials Science

Engineering, Multidisciplinary ; Materials Science, Biomaterials

WOS:001038205800001

MDPI

2-s2.0-85166309757

Scopus

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

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).