标题： 海失真：用于高分辨率航空测深测绘中水下图像恢复的数据集 显示英文标题

标题： Sea-Undistort: A Dataset for Through-Water Image Restoration in High Resolution Airborne Bathymetric Mapping

摘要： 由于动态水面、水柱特性以及太阳光照引入的复杂光学畸变，如波浪引起的图案、散射和眩光，浅水区域的精确基于图像的测深制图仍然具有挑战性。 在本工作中，我们引入了 Sea-Undistort，一个包含1200对512x512透过水场景的综合合成数据集，在Blender中渲染。 每对包括一个无畸变和一个有畸变的视图，具有真实的水效果，如阳光眩光、波浪和不同海底上的散射。 附带每张图像的元数据，如相机参数、太阳位置和平均深度，Sea-Undistort使监督训练成为可能，这在真实环境中是不可行的。 我们使用Sea-Undistort来评估两种最先进的图像恢复方法以及一个增强的轻量级基于扩散的框架，该框架采用早期融合的眩光掩码。 当应用于真实航空数据时，增强的扩散模型能够生成更完整的海底数字表面模型（DSMs），特别是在较深区域，减少测深误差，抑制眩光和散射，并清晰地恢复细小的海底细节。 数据集、权重和代码可在 https://www.magicbathy.eu/Sea-Undistort.html 公开获取。 显示英文摘要

摘要： Accurate image-based bathymetric mapping in shallow waters remains challenging due to the complex optical distortions such as wave induced patterns, scattering and sunglint, introduced by the dynamic water surface, the water column properties, and solar illumination. In this work, we introduce Sea-Undistort, a comprehensive synthetic dataset of 1200 paired 512x512 through-water scenes rendered in Blender. Each pair comprises a distortion-free and a distorted view, featuring realistic water effects such as sun glint, waves, and scattering over diverse seabeds. Accompanied by per-image metadata such as camera parameters, sun position, and average depth, Sea-Undistort enables supervised training that is otherwise infeasible in real environments. We use Sea-Undistort to benchmark two state-of-the-art image restoration methods alongside an enhanced lightweight diffusion-based framework with an early-fusion sun-glint mask. When applied to real aerial data, the enhanced diffusion model delivers more complete Digital Surface Models (DSMs) of the seabed, especially in deeper areas, reduces bathymetric errors, suppresses glint and scattering, and crisply restores fine seabed details. Dataset, weights, and code are publicly available at https://www.magicbathy.eu/Sea-Undistort.html.