Polarized reflection removal with dual-stream attention guidance

Addressing the challenge of removing reflections from images captured through glass surfaces holds significant importance across various practical applications. Existing methods often fall short due to reliance on intermediate predictions or special constraints, leading to undesirable artifacts. This paper addresses this issue by recognizing the inherent correlation between background and reflection. Leveraging the distinct optical properties of polarized images, we propose a novel dual-stream network with feature attention guidance for reflection removal. The model, taking multi-channel polarized images as input, exploits the optical characteristics of transmitted and reflected light to tackle the ill-posed problem. Our dual-stream structure predicts both reflectance and transmission images simultaneously, capitalizing on the unique relationship between these two elements. Additionally, we introduce the innovative differential feature fusion (DFF) block to enhance communication between the two streams. Our model shows superior performance to the state-of-the-art methods when tested on real-world polarization datasets. •A novel real-world dataset tailored for polarization-based reflection removal tasks.•Enhanced reflection removal via effective interaction within the dual-stream network.•A DFF block to efficiently use polarization cues and enhance dual-stream interaction.•Achievement of high-quality image estimation via an adversarial training strategy.