π-SUB：一种用于水下图像增强的物理感知合成水下基准数据集
$π$-SUB: A Physics-Informed Synthetic Underwater Benchmark Dataset for Underwater Image Enhancement
- Indian Institute of Science（印度科学学院）
- PES University（PES大学）
- Nanyang Technological University（南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出物理感知框架π-SUB构建合成水下基准数据集，在超真实感和泛化能力上优于现有数据集，可用于开发下一代水下图像增强方法。
AI中文摘要：
本文提出了π-SUB，这是一种用于生成合成水下基准数据集的物理感知框架，可缩小水下图像增强（UIE）领域中合成数据与真实数据之间的差距。该框架通过纳入与深度相关的下行辐照度、经生物解析的吸收特性以及所有10种Jerlov水质类型下的环境散射，再加上可独立控制的残余现象，扩展了经典的水下图像形成模型。利用该框架构建的π-SUB数据集包含成对的合成水下参考图像，涵盖从浅海到深海、从近岸到大洋的各类环境。为评估π-SUB，研究人员开展了大量模拟研究，涉及超真实感和泛化能力两项指标。在超真实感方面，π-SUB的全局Fréchet Inception距离（FID）比Syrea低46%；在泛化能力方面，研究人员选取了4种最先进的UIE架构（FUnIE-GAN、Pix2Pix、PUIE-Net和Phaseformer）对π-SUB进行对比评估。这些模型分别在6个数据集（含1个真实数据集和5个合成数据集）上训练，并在6个真实世界基准数据集上测试。在4种UIE架构和6个真实基准数据集的测试中，π-SUB的水下图像质量测量值（UIQM）比次优的PHISWID提升了4.18%，比Syrea提升了9.46%，同时噪声感知图像评估（NIQE）分别降低了48.78%和23.98%。这些结果表明，π-SUB是一种超真实感且泛化能力强的基准，可用于开发下一代水下图像增强方法，代码和数据集可在指定网址获取。
英文摘要：
This paper presents $π$-SUB, a physics-informed framework for generating synthetic underwater benchmark datasets that bridges the synthetic-to-real gap for Underwater Image Enhancement (UIE). The proposed framework extends the classical underwater image formation model by incorporating depth-dependent downwelling irradiance, biologically resolved absorption, and environmental scattering across all ten Jerlov water types, together with independently controllable residual phenomena. Using this framework, the $π$-SUB dataset consists of paired synthetic underwater-reference images spanning shallow-to-deep and coastal-to-oceanic environments. Extensive simulation studies have been carried out to evaluate $π$-SUB along two criteria namely hyper-realism and generalizability. For hyper-realism, $π$-SUB attains a global Frechet Inception Distance (FID) that is 46% lower than Syrea. For generalizability, four state-of-the-art UIE architectures (FUnIE-GAN, Pix2Pix, PUIE-Net, and Phaseformer) are used for comparative evaluation of $π$-SUB. These models were independently trained on six datasets including one real and five synthetic datasets and tested on six real-world benchmarks datasets. Across four UIE architectures and six real benchmark datasets, $π$-SUB improves UIQM by 4.18% over PHISWID (next best) and 9.46% over Syrea (next best), while reducing NIQE by 48.78% and 23.98%, respectively. These results establish $π$-SUB as a hyper-realistic and generalizable benchmark for developing the next generation of underwater image enhancement methods. The code and dataset are available at https://github.com/airl-iisc/pi-SUB
