AGTRL: Axis-Guided Trusted Representation Learning for Passive Hidden-Screen Recovery

When a screen is completely hidden from direct view, its emitted light can still diffuse onto a nearby wall and encode information about the displayed content. We investigate whether an ordinary camera can recover that content solely by observing these wall light traces. The central challenge is that propagation and diffuse reflection compress screen appearance into low-contrast brightness and color variations, mix contributions from different screen regions, and superimpose screen-related traces with wall texture, shadows, exposure variation, and sensor noise. We propose Axis-Guided Trusted Representation Learning (AGTRL) to recover hidden-screen content by learning both wall-to-screen correspondence and trace reliability. Axis-Guided Representation Learning (AGRL) organizes dispersed wall features along channel and spatial axes to form a screen-oriented representation. Embedded TraceTrust arbitration combines local continuity, artifact suspicion, and a learned gate to prioritize credible screen traces and suppress pseudo traces during reconstruction. We conduct extensive experiments across four screen-content families and under changes in distance, brightness, viewpoint, noise, occlusion, and wall appearance. AGTRL outperforms 20 mainstream image reconstruction methods in all 12 dataset–metric combinations and robustly recovers the principal color distributions, regional positions, page layouts, and structural contours of hidden screens. These results demonstrate the initial feasibility of observing and recovering hidden-screen content using an ordinary camera aimed only at wall light traces.