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References
The papers behind OXYS
The 22 papers of the project proposal, grouped by topic and numbered as in the proposal. Each entry opens the paper at its publisher, the open-access proceedings, arXiv or SSRN.
Image formation
- [1]A Revised Underwater Image Formation ModelD. Akkaynak, T. Treibitz · 2018 · CVPR 2018
- [2]Sea-Thru: A Method for Removing Water From Underwater ImagesD. Akkaynak, T. Treibitz · 2019 · CVPR 2019
- [4]Physics Informed and Data Driven Simulation of Underwater Images via Residual LearningT. Mondal, R. Mendoza, L. Drumetz · 2024 · arXiv 2402.05281
Illumination
- [3]Non-photorealistic Camera: Depth Edge Detection and Stylized Rendering Using Multi-Flash ImagingR. Raskar et al. · 2004 · ACM SIGGRAPH 2004
- [8]A Low-Cost Modulated Laser-Based Imaging System Using Square Ring Laser Illumination for Depressing Underwater BackscatterY. Hao et al. · 2024 · Photonics 11, 1070
- [9]Underwater Polarimetric Imaging System for Real-Time De-Backscatter Video ApplicationH. Wu et al. · 2026 · Preprint, SSRN 5604787 (submitted to Optics & Laser Technology)
- [10]Physics-Informed Neural Network for Polarimetric Underwater ImagingH. Hu et al. · 2022 · Optics Express 30(13), 22512
- [11]Range Intensity Profiles of Multi-Slice Integration for Pulsed Laser Range-Gated Imaging SystemH. Lin et al. · 2022 · Photonics 9, 505
- [12]Polarization-Enhanced Underwater Laser Range-Gated Imaging for Subaquatic ApplicationsS. Chen et al. · 2024 · Sensors 24, 6681
Particles
- [13]Real-Time Marine Snow Noise Removal from Underwater Video SequencesB. Cyganek, K. Gongola · 2018 · Journal of Electronic Imaging 27(4)
- [14]A Multi-Stage Progressive Network with Feature Transmission and Fusion for Marine Snow RemovalL. Liu, Y. Liao, B. He · 2024 · Sensors 24, 356
- [15]A Deep Learning Approach for Marine Snow Synthesis and RemovalF. Galetto, G. Deng · 2023 · arXiv 2311.15584
- [16]Detecting and Suppressing Marine Snow for Underwater Visual SLAML. M. Hodne et al. · 2022 · CVPR Workshops 2022
- [17]Marine Snow Removal Using Internally Generated Pseudo Ground TruthA. Malyugina et al. · 2025 · arXiv 2504.19289
Sonar fusion
- [18]Uncertainty Aware Mapping for Vision-Based Underwater RobotsA. Bhowmik et al. · 2025 · arXiv 2507.10991
- [19]Two-Stage Probabilistic 3D Reconstruction of Underwater Objects via Range-Adaptive Sonar and Optical Fusion in Turbid EnvironmentsJ. Kim et al. · 2026 · Int. J. Control, Automation and Systems 24
- [20]Sonar-MASt3R: Real-Time Opti-Acoustic Fusion in Turbid, Unstructured EnvironmentsA. Phung, R. Camilli · 2026 · arXiv 2603.13585
- [21]RUSSO: Robust Underwater SLAM with Sonar Optimization against Visual DegradationS. Pan et al. · 2025 · arXiv 2503.01434
Turbidity
- [5]Estimation of Underwater Visibility in Coastal and Inland Waters Using Remote Sensing DataA. Kulshreshtha, P. Shanmugam · 2017 · Environmental Monitoring and Assessment
- [6]Estimating the Underwater Diffuse Attenuation Coefficient with a Low-Cost Instrument: The KdUINO DIY BuoyR. Bardaji et al. · 2016 · Sensors 16(3), 373
- [7]Turbidivision: A Machine Vision Application for Estimating Turbidity from Underwater ImagesI. M. Rudy, M. J. Wilson · 2024 · PeerJ
Survey
Further reading
The classic models behind the physics stages and the self-calibration study.
- A Computer Model for Underwater Camera SystemsB. L. McGlamery · 1980 · Proc. SPIE 0208
- Computer Modeling and the Design of Optimal Underwater Imaging SystemsJ. S. Jaffe · 1990 · IEEE J. Oceanic Engineering 15(2)
- Clear Underwater VisionY. Y. Schechner, N. Karpel · 2004 · CVPR 2004
- Improving Color Correction for Underwater Image SurveysJ. W. Kaeli, H. Singh, C. Murphy et al. · 2011 · OCEANS 2011
- True Color Correction of Autonomous Underwater Vehicle ImageryM. Bryson et al. · 2016 · Journal of Field Robotics
- Sea-ing Through Scattered Rays: Revisiting the Image Formation Model for Realistic Underwater Image GenerationV. Ismiroglou et al. · 2025 · arXiv 2509.15011