arXiv 2504.109742025-05-29cs.CVeess.IV

前视声纳图像的自监督增强：通过特征空间变换与多帧融合弥合跨模态退化差距

Self-Supervised Enhancement of Forward-Looking Sonar Images: Bridging Cross-Modal Degradation Gaps through Feature Space Transformation and Multi-Frame Fusion

College of Electronic Engineering, Naval University of Engineering（电子工程学院，海军工程大学）；College of Meteorology and Oceanography, National University of Defense Technology（气象海洋学院，国防科技大学）；College of Computer Science and Technology, National University of Defense Technology（计算机科学与技术学院，国防科技大学）；Department of Computer Science and Technology, Tsinghua University（计算机科学与技术系，清华大学）

Zhisheng Zhang, Peng Zhang, Fengxiang Wang, Liangli Ma, Fuchun Sun

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AI总结：

提出一种基于特征空间变换与多帧融合的自监督方法，有效弥合声纳与遥感图像间的跨模态退化差距，显著提升前视声纳图像的增强效果与水下目标检测性能。

摘要：

增强前视声纳图像对于准确的水下目标检测至关重要。当前的深度学习方法主要依赖仿真数据进行监督训练，但获取高质量真实世界配对数据的困难限制了其实际应用和泛化能力。尽管遥感领域的自监督方法部分缓解了数据短缺问题，但它们忽视了声纳与遥感图像之间的跨模态退化差距。直接迁移预训练权重通常会导致声纳图像过度平滑、细节丢失以及亮度不足。为此，我们提出了一种特征空间变换，将声纳图像从像素域映射到鲁棒特征域，有效弥合退化差距。此外，我们的自监督多帧融合策略利用帧间互补信息，自然去除散斑噪声并增强目标区域亮度。在三个自采集的真实世界前视声纳数据集上的实验表明，我们的方法显著优于现有方法，有效抑制噪声、保留细节边缘并大幅提升亮度，展现出在水下目标检测应用中的巨大潜力。

英文摘要：

Enhancing forward-looking sonar images is critical for accurate underwater target detection. Current deep learning methods mainly rely on supervised training with simulated data, but the difficulty in obtaining high-quality real-world paired data limits their practical use and generalization. Although self-supervised approaches from remote sensing partially alleviate data shortages, they neglect the cross-modal degradation gap between sonar and remote sensing images. Directly transferring pretrained weights often leads to overly smooth sonar images, detail loss, and insufficient brightness. To address this, we propose a feature-space transformation that maps sonar images from the pixel domain to a robust feature domain, effectively bridging the degradation gap. Additionally, our self-supervised multi-frame fusion strategy leverages complementary inter-frame information to naturally remove speckle noise and enhance target-region brightness. Experiments on three self-collected real-world forward-looking sonar datasets show that our method significantly outperforms existing approaches, effectively suppressing noise, preserving detailed edges, and substantially improving brightness, demonstrating strong potential for underwater target detection applications.

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