Deep Joint Source-Channel Coding of Underwater Image Enhancement in AUV Swarms

IF 3.7 3区计算机科学 Q2 TELECOMMUNICATIONS

IEEE Communications Letters Pub Date : 2025-04-01 DOI:10.1109/LCOMM.2025.3556673

Zhenguo Zhang;Guang Liu;Minghui Wang;Bo Chen;Zesheng Liu;Xiaojie Zhang

引用次数: 0

Abstract

Underwater images commonly suffer from visual degradation due to the complexities of the underwater imaging and transmission environments. To address this issue, we propose a lightweight system for underwater image enhancement and transmission tailored for autonomous underwater vehicle swarms, improving both image quality and communication efficiency with surface equipment. The proposed model enables a target-oriented multi-space feature encoding network to process degraded pixels, followed by a reinforcement learning-based bandwidth allocation network that optimizes feature distribution under constrained bandwidth conditions. Simulation results demonstrate that the proposed system significantly enhances the speed and effectiveness of image recovery under varying channel conditions.

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水下航行器群水下图像增强的深度联合信源信道编码

由于水下成像和传输环境的复杂性，水下图像通常会出现视觉退化。为了解决这个问题，我们提出了一种轻量级的水下图像增强和传输系统，为自主水下航行器群量身定制，提高了图像质量和与水面设备的通信效率。该模型首先利用面向目标的多空间特征编码网络处理退化像素，然后利用基于强化学习的带宽分配网络在受限带宽条件下优化特征分布。仿真结果表明，在不同信道条件下，该系统显著提高了图像恢复的速度和有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Communications Letters 工程技术-电信学

CiteScore

8.10

自引率

7.30%

发文量

590

审稿时长

2.8 months

期刊介绍： The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.