LEPF-Net: Light Enhancement Pixel Fusion Network for Underwater Image Enhancement

被引:0
|
作者
Yan, Jiaquan [1 ]
Wang, Yijian [2 ]
Fan, Haoyi [3 ]
Huang, Jiayan [4 ]
Grau, Antoni [5 ]
Wang, Chuansheng [5 ]
机构
[1] Minjiang Univ, Coll Comp & Control Engn, Fujian Prov Key Lab Informat Proc & Intelligent Co, Fuzhou 350121, Peoples R China
[2] Minjiang Univ, Coll Math & Data Sci, Fuzhou 350108, Peoples R China
[3] Zhengzhou Univ, Sch Comp & Artificial Intelligence, Zhengzhou 450001, Peoples R China
[4] Putian Univ, New Engn Ind Coll, Putian 351100, Peoples R China
[5] Univ Politecn Cataluna, Dept Automatic Control Tech, Barcelona 08034, Spain
关键词
underwater image enhancement; light enhancement curve; pixel fusion; adaptive feature weights; RESTORATION; WATER;
D O I
10.3390/jmse11061195
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
Underwater images often suffer from degradation due to scattering and absorption. With the development of artificial intelligence, fully supervised learning-based models have been widely adopted to solve this problem. However, the enhancement performance is susceptible to the quality of the reference images, which is more pronounced in underwater image enhancement tasks because the ground truths are not available. In this paper, we propose a light-enhanced pixel fusion network (LEPF-Net) to solve this problem. Specifically, we first introduce a novel light enhancement block (LEB) based on the residual block (RB) and the light enhancement curve (LE-Curve) to restore the cast color of the images. The RB is adopted to learn and obtain the feature maps from an original input image, and the LE-Curve is used to renovate the color cast of the learned images. To realize the superb detail of the repaired images, which is superior to the reference images, we develop a pixel fusion subnetwork (PF-SubNet) that adopts a pixel attention mechanism (PAM) to eliminate noise from the underwater image. The PAM adapts weight allocation to different levels of a feature map, which leads to an enhancement in the visibility of severely degraded areas. The experimental results show that the proposed LEPF-Net outperforms most of the existing underwater image enhancement methods. Furthermore, among the five classic no-reference image quality assessment (NRIQA) indicators, the enhanced images obtained by LEPF-Net are of higher quality than the ground truths from the UIEB dataset.
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页数:20
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