Lightweight Underwater Image Enhancement Network Based on Cross-Scale Deep Distillation Feature Perception

doi:10.3969/j.issn.1673-6141.2024.03.010

Journal of Atmospheric and Environmental Optics ›› 2024, Vol. 19 ›› Issue (3): 381-390.doi: 10.3969/j.issn.1673-6141.2024.03.010

Lightweight Underwater Image Enhancement Network Based on Cross-Scale Deep Distillation Feature Perception

WU Xiaohua 1, LI Zenglu 2,3, XU Zhanghua 4, ZHOU Jingchun 5*

1 School of Art & Design, Sanming University, Sanming 365004, China; 2 Network Technology Center, Sanming University, Sanming 365004, China; 3 Fujian Provincial Key Laboratory of Resources and Environment Monitoring & Sustainable Management and Utilization, Sanming University, Sanming 365004, China; 4 Academy of Geography and Ecological Environment, Fuzhou University, Fuzhou 350108, China; 5 Information Science and Technology College, Dalian Maritime University, Dalian 116026, China

Received:2023-10-31 Revised:2024-02-03 Online:2024-05-28 Published:2024-06-11

Abstract

Abstract: Underwater image enhancement technology has the potential to improve the quality and visibility of underwater images, which has important application value in enriching digital media resources, underwater exploration, underwater communication, and other fields. In recent years, deep learning methods have achieved remarkable results in underwater image enhancement. However, the existing methods suffer from high computational complexity, which limits their application in scenarios with limited computational resources. To address this problem, a lightweight underwater image enhancement method is proposed. The method is based on cross-scale depth distillation feature perception and adopts a U-shaped network structure, which can significantly reduce the model parameter size while ensuring the extraction of nonlinear abstract layers. The experimental results show that the proposed method has achieved competitive results in both visual effects and objective evaluation metrics.

Key words: underwater image enhancement, lightweight, cross-scale, distillation feature, U-shaped network

CLC Number:

TP391

WU Xiaohua , LI Zenglu , XU Zhanghua , ZHOU Jingchun . Lightweight Underwater Image Enhancement Network Based on Cross-Scale Deep Distillation Feature Perception[J]. Journal of Atmospheric and Environmental Optics, 2024, 19(3): 381-390.

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Lightweight Underwater Image Enhancement Network Based on Cross-Scale Deep Distillation Feature Perception

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