Explicit semantic guidance for single image reflection removal via perceptual influence modeling

Reflection artifacts caused by photographing through glass often degrade image visibility and impair downstream visual tasks. Single Image Reflection Removal (SIRR) remains challenging due to its ill-posed nature and the entangled appearance of reflection and transmission layers. While recent methods explore semantic priors, most rely on implicit feature fusion without explicitly modeling the perceptual disturbance. To address this, we introduce the Perceptual Reflection Influence Map (PRIM)-a luminance-based, relative measure that captures the spatial distribution and intensity of reflection-induced interference. PRIM serves as an explicit supervision signal, guiding the network to focus on perceptually sensitive regions. Building on this, we design a PRIM-Adaptive Fusion Module (PAFM) to dynamically integrate semantic and local features using PRIM-derived cues. Furthermore, we propose a physics-inspired Reflection Removal Unit (RRU) that leverages both statistical frequency-domain priors and the physical image formation model to enable robust feature disentanglement. Extensive experiments on multiple real-world SIRR benchmarks demonstrate that our method achieves state-of-the-art performance, validating the effectiveness of our semantic-guided and physics-inspired framework.