改进水下成像模型，更真实生成浑浊环境图像。
Sea-ing Through Scattered Rays: Revisiting the Image Formation Model for Realistic Underwater Image Generation
- 引入前向散射项，考虑非均匀介质影响
- 在高浑浊度下质量提升，82.5%参与者偏好新模型
- 发布含参考图的BUCKET数据集，适合水下视觉研究
近年来，水下图像形成模型被广泛用于合成水下数据生成。尽管许多方法聚焦于颜色失真问题，却常忽略高度浑浊环境中距离相关的可见度损失。本文提出一种改进的合成数据生成流程，包含常被忽略的前向散射项，并考虑非均匀介质。同时，在受控浑浊条件下采集了BUCKET数据集，获得对应参考图像的真实浑浊视频。结果表明，相比基准模型，本方法在浑浊度升高时表现显著提升，调查参与者中82.5%选择新模型。数据与代码可访问项目页：vap.aau.dk/sea-ing-through-scattered-rays。
原文摘要 · Abstract (English)
In recent years, the underwater image formation model has found extensive use in the generation of synthetic underwater data. Although many approaches focus on scenes primarily affected by discoloration, they often overlook the model's ability to capture the complex, distance-dependent visibility loss present in highly turbid environments. In this work, we propose an improved synthetic data generation pipeline that includes the commonly omitted forward scattering term, while also considering a nonuniform medium. Additionally, we collected the BUCKET dataset under controlled turbidity conditions to acquire real turbid footage with the corresponding reference images. Our results demonstrate qualitative improvements over the reference model, particularly under increasing turbidity, with a selection rate of 82.5% by survey participants. Data and code can be accessed on the project page: vap.aau.dk/sea-ing-through-scattered-rays.
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