用于水下图像恢复的单阶段隐形扩散
IEEE transactions on pattern analysis and machine intelligence
|August 15, 2025
概括
本研究介绍了SLURPP,这是一种用于水下图像修复的新型网络,可以显著提高复杂场景的速度和性能. 它克服了现有方法的局限性,为海洋和水下应用提供更好的质量.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 海洋技术 海洋技术
背景情况:
- 水下图像由于光衰减和散射而遭受色彩损失,低对比度和可见性降低.
- 现有的基于扩散的修复方法在计算上昂贵,并在复杂的水下场景中产生文物.
- 准确的恢复对于海洋生态,水产养殖,建筑和考古学至关重要.
研究的目的:
- 为复杂场景开发一个计算高效和有效的水下图像恢复算法.
- 解决现有的基于像素域扩散的方法在处理深度变化和几何复杂性的局限性.
- 为了提高各种应用的水下图像的视觉质量和准确性.
主要方法:
- 介绍了SLURPP,一种新的网络架构,将潜在扩散模型与显式场景分解相结合.
- 开发了一种基于物理的合成数据生成管道,用于实现现实的水下图像退化.
- 经过训练的SLURPP使用多种多样的注释合成数据捕捉各种水下条件.
主要成果:
- 在合成和现实世界的基准指标上,SLURPP实现了最先进的性能.
- 与现有的基于扩散的方法相比,证明了超过200倍的速度改进.
- 在合成基准数据上实现了大约3dB的PSNR改进,在真实数据上获得了令人信服的定性结果.
结论:
- 在水下图像恢复方面,SLURPP提供了显著的进步,平衡速度和质量.
- 新的架构和合成数据生成管道有效地处理复杂的水下环境.
- 这种方法为改善科学和工业应用的水下成像提供了强大的工具.
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