Deep Underwater Image Quality Assessment via Progressive Physics-Aware Multi-Prior Collaboration

Underwater image quality assessment (UIQA) is a critical research area, challenged by underwater environments such as wavelength-dependent light attenuation, scattering, and non-uniform illumination. Existing deep learning-based UIQA methods often address these degradations in isolation, neglecting their complex interplay with human perception and lacking explicit modeling of underwater optical phenomena. To address this, we propose PhysIQ-Net, a novel framework that integrates physics-driven principles with progressive multi-prior interaction modeling through three key innovations: First, introduce dual physics-based decomposition that separates images into Backscatter, Transmission, Reflectance, and Illuminance components to capture distinct degradation mechanisms; Second, propose prior-guided dynamic filtering that adapts convolutional kernels to image-specific content using physical priors; and Third, propose physic-informed Cross-Domain Feature Interaction that enables bidirectional collaboration between color-aware and structure-aware representations to model their perceptual inter-dependencies. Extensive experiments across multiple benchmark datasets demonstrate that PhysIQ-Net significantly outperforms existing methods, with ablation studies validating each component’s contribution, providing a robust solution for UIQA.