基于物理成像的水下图像质量评估框架，提升感知准确性
PIGUIQA: A Physical Imaging Guided Perceptual Framework for Underwater Image Quality Assessment
- 融合水下辐射传输理论，量化透射衰减与后向散射影响
- 引入邻域注意力模块，捕捉局部细微特征提升感知灵敏度
- 结合全局感知聚合器，实现跨数据集稳定高精度评估
本文提出一种物理成像引导的水下图像质量评估框架（PIGUIQA）。首先，将水下图像质量评估建模为同时考虑直接透射衰减与后向散射共同影响的综合问题，基于水下辐射传输理论，系统性地整合物理成像估计，建立上述失真现象的定量指标。其次，针对图像内容重要性与人眼感知敏感度的空间差异，设计基于邻域注意力机制的局部感知模块，有效捕捉图像中的细微特征，从而在局部信息基础上增强对失真的自适应感知能力。第三，通过全局感知聚合器进一步融合整体场景信息与水下失真特征，实现图像质量评分的精准预测。在多个基准数据集上的大量实验表明，PIGUIQA在保持强跨数据集泛化能力的同时，达到当前最优性能。代码已公开于 https://github.com/WeizhiXian/PIGUIQA。
原文摘要 · Abstract (English)
In this paper, we propose a Physical Imaging Guided perceptual framework for Underwater Image Quality Assessment (UIQA), termed PIGUIQA. First, we formulate UIQA as a comprehensive problem that considers the combined effects of direct transmission attenuation and backward scattering on image perception. By leveraging underwater radiative transfer theory, we systematically integrate physics-based imaging estimations to establish quantitative metrics for these distortions. Second, recognizing spatial variations in image content significance and human perceptual sensitivity to distortions, we design a module built upon a neighborhood attention mechanism for local perception of images. This module effectively captures subtle features in images, thereby enhancing the adaptive perception of distortions on the basis of local information. Third, by employing a global perceptual aggregator that further integrates holistic image scene with underwater distortion information, the proposed model accurately predicts image quality scores. Extensive experiments across multiple benchmarks demonstrate that PIGUIQA achieves state-of-the-art performance while maintaining robust cross-dataset generalizability. The implementation is publicly available at https://github.com/WeizhiXian/PIGUIQA
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