构建首个物理驱动的水下图像增强合成数据集，提升真实感与泛化能力。
$π$-SUB: A Physics-Informed Synthetic Underwater Benchmark Dataset for Underwater Image Enhancement
- 基于物理模型生成含深度、水质和散射特性的合成水下图像
- 真实感比Syrea高46%（FID降低），图像质量提升9.46%（UIQM）
- 适合研发新一代水下图像增强算法的研究者使用
本文提出π-SUB，一个物理信息驱动的合成水下基准数据集框架，旨在弥合合成到真实场景的差距。该框架扩展了经典水下成像模型，引入深度相关的下行辐照度、生物解析的吸收特性及十类杰尔洛夫水体中的环境散射，并支持独立控制残余现象。基于此框架，π-SUB包含从浅海到深海、近岸到远洋环境的配对合成-参考图像。通过大量仿真评估，π-SUB在超真实感与泛化能力两方面表现优异：在超真实感上，全局弗雷切特起始距离（FID）比Syrea低46%；在泛化性上，四种先进UIE架构（FUnIE-GAN、Pix2Pix、PUIE-Net、Phaseformer）在六组真实基准测试中，相较次优数据集PHISWID提升UIQM达4.18%，相较Syrea提升9.46%，同时降低NIQE 48.78%与23.98%。结果表明π-SUB是下一代水下图像增强方法的理想基准。代码与数据集见https://github.com/airl-iisc/pi-SUB。
原文摘要 · Abstract (English)
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
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