Underwater Imaging

Follow this topic Ask about this topic

Underwater imaging studies how cameras and sonar sense scenes when light is absorbed and scattered, visibility is low, and images may be distorted by refraction or noise. Methods include physics-based restoration and neural enhancement, object detection and segmentation, sonar processing, and 3D reconstruction using techniques such as structure-from-motion, neural radiance fields, and Gaussian splatting. Researchers also use synthetic data, real-world benchmarks, self-supervised learning, and optical-acoustic fusion to address limited labels and changing conditions. A central challenge is making methods generalize across water clarity, lighting, sensors, and environments while improving not just visual quality but also geometry and downstream perception.

Latest papers

72 papers6 papers in the last four weeks, down 14% on the four weeks before. 0.0% of all new papers.

Mine-JEPA: In-Domain Self-Supervised Learning for Mine-Like Object Classification in Side-Scan Sonar
Apr 1, 2026Taeyoun Kwon, Youngwon Choi, Hyeonyu Kim +3Classification Self-Supervised Learning
Maum AI Inc. · Seoul National University · KAIST +1
UW-VOS: A Large-Scale Dataset for Underwater Video Object Segmentation
Mar 25, 2026Hongshen Zhao, Jingkang Tai, Yuhang Wu +5Video Object Segmentation Underwater Imaging
Southeast University
CTFS : Collaborative Teacher Framework for Forward-Looking Sonar Image Semantic Segmentation with Extremely Limited Labels
Mar 22, 2026Ping Guo, Chengzhou Li, Guanchen Meng +6Image Segmentation Semantic Segmentation
School of Software Technology, Dalian University of Technology, Dalian, China
USIS-PGM: Photometric Gaussian Mixtures for Underwater Salient Instance Segmentation
Mar 14, 2026Lin Hong, Xiangtong Yao, Mürüvvet Bozkurt +2Image Segmentation Instance Segmentation
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China · School of CIT, Chair i6, Technical University of Munich, Munich, Germany · School of CIT, Technical University of Munich, Munich, Germany +1
Sonar-MASt3R: Real-Time Opti-Acoustic Fusion in Turbid, Unstructured Environments
Mar 13, 2026Amy Phung, Richard CamilliUnderwater 3D Reconstruction 3D Reconstruction
Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Deep Submergence Laboratory, Woods Hole, MA, USA · Massachusetts Institute of Technology, Cambridge, MA, USA
Empowering Semantic-Sensitive Underwater Image Enhancement with VLM
Mar 13, 2026Guodong Fan, Shengning Zhou, Genji Yuan +3Geometry-Guided Image Restoration Image Enhancement
Shandong Technology and Business University, Yantai, China · Shandong University of Finance and Economics, Jinan, China · Dalian Maritime University, Dalian, China
Looking Into the Water by Unsupervised Learning of the Surface Shape
Mar 8, 2026Ori Lifschitz, Tali Treibitz, Dan RosenbaumUnsupervised Image Restoration Image Restoration
Hatter Department of Marine Technologies, Charney School of Marine Sciences, University of Haifa, Haifa, Israel · Department of Computer Science, University of Haifa, Haifa, Israel
AQUA-Net: Adaptive Frequency Fusion and Illumination Aware Network for Underwater Image Enhancement
Dec 5, 2025Munsif Ali, Najmul Hassan, Lucia Ventura +3Image Enhancement Underwater Image Enhancement
Stazione Zoologica Anton Dohrn, Villa Comunale, 80121 Napoli, Italy. · School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Japan.
BenthiCat: An opti-acoustic dataset for advancing benthic classification and habitat mapping
Oct 6, 2025Hayat Rajani, Valerio Franchi, Borja Martinez-Clavel Valles +3Underwater Imaging
Computer Vision and Robotics Research Institute, University of Girona, Spain · Tecnoambiente SLU, Spain
Synthetic Enclosed Echoes: A New Dataset to Mitigate the Gap Between Simulated and Real-World Sonar Data
May 21, 2025Guilherme de Oliveira, Matheus M. dos Santos, Paulo L. J. Drews-JrSynthetic-to-Real Domain Adaptation Underwater Imaging
NAUTEC - Intelligent robotics and automation group, FURG - Federal University of Rio Grande, Rio Grande, RS, Brazil · ICMC - Institute of Mathematics and Computer Sciences, USP - University of S˜ao Paulo, S˜ao Carlos, SP, Brazil
TUGS: Physics-based Compact Representation of Underwater Scenes by Tensorized Gaussian
May 12, 2025Shijie Lian, Ziyi Zhang, Hua Li +43D Gaussian Splatting Underwater Imaging
1Huazhong University of Science and Technology · 2Zhongguancun Academy · 3The Chinese University of Hong Kong, Shenzhen +3
UWA360CAM: A 360 $^{\circ}$ 24/7 Real-Time Streaming Camera System for Underwater Applications
Sep 22, 2023Quan-Dung Pham, Yipeng Zhu, Tan-Sang Ha +3Underwater Imaging
Hong Kong University of Science and Technology, Hong Kong SAR. · Trinity College Dublin, Ireland.

Apr 1, 2026J/K move · Enter open · S save

Mine-JEPA: In-Domain Self-Supervised Learning for Mine-Like Object Classification in Side-Scan Sonar

Taeyoun Kwon, Youngwon Choi, Hyeonyu Kim, Myeongkyun Cho+2

Maum AI Inc. · Seoul National University · KAIST+1

Classification Self-Supervised Learning Vision Foundation Model Adaptation Underwater Imaging Joint-Embedding Predictive Architecture

Open paper AskPDF arXiv

Side-scan sonar (SSS) mine classification is a challenging maritime vision problem characterized by extreme data scarcity and a large domain gap from natural images. While self-supervised learning (SSL) and general-purpose vision foundation models have shown strong performance in general vision and several specialized domains, their use in SSS remains largely unexplored. We present Mine-JEPA, the first in-domain SSL pipeline for SSS mine classification, using SIGReg, a regularization-based SSL loss, to pretrain on only 1,170 unlabeled sonar images. In the binary mine vs. non-mine setting, Mine-JEPA achieves an F1 score of 0.935, outperforming fine-tuned DINOv3 (0.922), a foundation model pretrained on 1.7B images. For 3-class mine-like object classification, Mine-JEPA reaches 0.820 with synthetic data augmentation, again outperforming fine-tuned DINOv3 (0.810). We further observe that applying in-domain SSL to foundation models degrades performance by 10--13 percentage points, suggesting that stronger pretrained models do not always benefit from additional domain adaptation. In addition, Mine-JEPA with a compact ViT-Tiny backbone achieves competitive performance while using 4x fewer parameters than DINOv3. These results suggest that carefully designed in-domain self-supervised learning is a viable alternative to much larger foundation models in data-scarce maritime sonar imagery.