Diffusion-based dual-view reflection removal

Reflection removal aims to recover clean images from pho-tographs degraded by glass, a crucial problem in mobile photography.Single-image methods suffer from the fundamental ill-posedness of de-composition and often rely on specialized hardware to enhance robust-ness. We propose a user-friendly dual-view diffusion framework that ef-fectively leverages complementary viewpoint information for ubiquitousdual-camera devices. Our approach introduces frame-aware spatial at-tention to capture implicit cross-view correlations and integrates camerapose as conditional embeddings for geometric guidance. We contributeDualRef, a dataset for reflection removal comprising semi-syntheticpairs and real-world dual-camera sequences with ground truth. Exper-iments demonstrate over 10% PSNR improvement on real-world data,significantly outperforming state-of-the-art methods.