Sea-Undistort: A Synthetic Dataset for Restoring Through-Water Images in Airborne Bathymetric Mapping

The dataset To address the absence of real-world paired imagery with and without wave- and water-induced distortions, we introduce Sea-Undistort, a synthetic dataset created using the open-source 3D graphics platform Blender. The dataset comprises 1200 image pairs, each consisting of 512×512 pixel RGB renderings of shallow underwater scenes. Every pair includes a “non-distorted” image, representing minimal surface and column distortions, and a corresponding “distorted” version that incorporates realistic optical phenomena such as sun glint, wave-induced deformations, turbidity, and light scattering. These effects are procedurally generated to replicate the diverse challenges encountered in through-water imaging for bathymetry. The scenes are designed with randomized combinations of typical shallow-water seabed types, including rocky outcrops, sandy flats, gravel beds, and seagrass patches, capturing a wide range of textures, reflectance patterns, and radiometric conditions. Refraction is accurately modeled in both the distorted and non-distorted images to maintain geometric consistency with real underwater imaging physics. In addition, camera settings are uniformly sampled within specific ranges to ensure diverse imaging conditions. Sensor characteristics include a physical width of 36 mm and effective pixel widths of 4000 or 5472 pixels. Focal lengths of 20 mm and 24 mm are simulated with only the central 512x512 pixels rendered. Camera altitude ranges from 30 m to 200 m, resulting in a ground sampling distance (GSD) between 0.014 m and 0.063 m. Average depths range from –0.5 m to –8 m, with a maximum tilt angle of 5°. Sun elevation angles between 25° and 70°, along with varying atmospheric parameters (e.g., air, dust), are used to simulate different illumination conditions. Generated images are accompanied by a .json file containing this metadata per image. Sea-Undistort is designed to support supervised training of deep learning models for through-water image enhancement and correction, enabling generalization to real-world conditions where undistorted ground truth is otherwise unobtainable. Citation If you use the dataset, please cite: AcknowledgmentThis work was part of the project MagicBathy which is a research project funded by the European Commission for the period 2023-2025. It is funded under the HORIZON Europe MSCA Postdoctoral Fellowships - European Fellowships (GA 101063294).针对当前缺乏带与不带波浪及水体诱导畸变的真实配对影像数据集的现状，我们提出Sea-Undistort——一款基于开源3D图形平台Blender构建的合成数据集。 该数据集包含1200组影像对，每组均由512×512像素的浅海水下场景RGB渲染图组成。每组影像对均包含一张"无畸变"图像与一张对应的"带畸变"图像：前者仅存在极少量的水面与水体柱面畸变，后者则集成了太阳耀斑、波浪诱导形变、水体浊度与光散射等真实光学效应。这些效应均通过程序化生成，用以复现测深学领域穿水成像所面临的各类实际挑战。 场景采用典型浅海海底类型的随机组合进行构建，涵盖岩礁、沙质平原、砾石床与海草床等，覆盖了多样的纹理、反射模式与辐射特性。无论是畸变图像还是无畸变图像，均精确建模了折射效应，以确保与真实水下成像物理规律保持几何一致性。 此外，相机参数会在特定范围内进行均匀采样，以保障成像条件的多样性。传感器特性包括36mm的物理宽度，以及4000或5472像素的有效像素宽度；模拟了20mm与24mm两种焦距，且仅渲染图像中央的512×512像素区域。相机高度范围为30m至200m，对应的地面采样距离（GSD）介于0.014m至0.063m之间。平均水深范围为–0.5m至–8m，最大倾斜角为5°。太阳高度角范围为25°至70°，同时结合可变的大气参数（如空气、尘埃），用以模拟不同的光照环境。生成的影像会附带一个.json元数据文件，其中包含每张图像的上述参数信息。 Sea-Undistort旨在为穿水图像增强与校正任务的深度学习模型监督训练提供支持，使其能够泛化至那些无法获取无畸变真值的真实应用场景。 ## 引用 若使用本数据集，请引用如下： ## 致谢 本研究隶属于MagicBathy项目——该项目由欧盟委员会资助，执行周期为2023-2025年，资助来源为"地平线欧洲（HORIZON Europe）MSCA博士后奖学金——欧洲奖学金"项目（项目编号GA 101063294）。