cs.AI updates on arXiv.org 09月19日
水下图像生成模型改进与数据集
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本文提出一种改进的水下图像生成模型,包括被忽视的前向散射项,并考虑非均匀介质。同时,收集了受控混浊条件下的BUCKET数据集,以获取真实混浊场景图像。结果显示,模型在混浊度增加的情况下表现优于参考模型,调查参与者选择率高达82.5%。

arXiv:2509.15011v1 Announce Type: cross Abstract: 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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水下图像生成 模型改进 数据集
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