农业机械学报2026，Vol.57Issue(19)：95-102,238,9.DOI:10.6041/j.issn.1000-1298.2026.19.009
基于时域-频域协同引导的海洋机器人视觉感知增强方法
Time-frequency Collaborative Guidance-based Visual Perception Enhancement for Marine Robots
摘要
Abstract
Marine robots often capture severely degraded images during underwater exploration and operation tasks due to light absorption and scattering in water,which leads to low brightness,blurred details,and significant color distortion.These degradations substantially hinder subsequent visual perception and intelligent decision tasks.To solve the limitations of existing approaches that mainly rely on time-domain modeling and neglect the role of frequency-domain features in structure preservation and detail recovery,the temporal-frequency collaborative guided network was proposed for marine robots.The proposed network consisted of two stages:time-frequency feature extraction and multi-dimensional feature refinement.In the time-frequency feature extraction stage,a time domain branch and a frequency domain branch were employed to capture complementary representations,and a cross domain collaboration module was introduced to explicitly fuse multi domain features,thereby strengthening the representation of underwater image structures and textures.In the multi-dimensional feature refinement stage,several enhancement modules were incorporated to further improve reconstruction quality.Specifically,polarization residual module extracted multi-directional features and performed adaptive fusion were proposed to emphasize effective structural information while suppressing noise,which enabled simultaneous enhancement of image details and contrast.In addition,dual attention coupling module combined channel attention and spatial attention was introduced to reinforce local salient details while preserving global structural consistency.Finally,multi-scale residual module with dual receptive fields integrated multi-scale convolutions,attention modulation,and learnable scaling parameters was proposed to adaptively highlight critical features and balance fine-detail preservation with overall visual quality.Extensive experiments on multiple public underwater image datasets showed that the proposed method consistently outperformed state-of-the-art visual enhancement approaches in subjective visual quality,and demonstrated strong robustness and generalization capability in complex underwater environments,effectively supporting marine robot visual perception and downstream tasks.关键词
海洋机器人/感知增强/卷积网络/特征融合/特征提取/深度学习Key words
marine robot/perception enhancement/convolutional network/feature fusion/feature extraction/deep learning分类
信息技术与安全科学引用本文复制引用
纪勋,戴成菘,李晓琳,赵昊中,张云泽,郝立颖..基于时域-频域协同引导的海洋机器人视觉感知增强方法[J].农业机械学报,2026,57(19):95-102,238,9.基金项目
国家自然科学基金项目(52501429、52171292)、辽宁省自然科学基金项目(2025-BS-0210)和辽宁省教育厅高校基本科研业务项目(LJ212410151006) （52501429、52171292）
