ReGLaR: Learning Reflection-Guided Latent Representations for Hidden Screen Content Recovery from a Diffuse Wall-Reflection Image

Can an ordinary camera recover a hidden screen's displayed content from a red, green, and blue (RGB) image of its reflection on a visible wall? Diffuse reflection spatially mixes and attenuates the screen signal, while wall texture, ambient illumination, and camera noise obscure the remaining content cues. Recovery therefore requires turning the wall observation into screen content while retaining the useful information carried by the reflection. We propose Reflection-Guided Latent Representation Learning (ReGLaR) to address these requirements. Measurement-to-Content Latent Representation Learning (MCLRL) aligns a transitional wall-side latent with a fixed clean-content reference, guiding intermediate features toward recovering screen content. Low-Order Reflection Consistency Supervision (LRCS) extracts low-pass RGB, luminance, and slow spatial variation from the observed wall. It compares them with a learned proxy generated from the reconstruction in a shared coarse space. Experiments cover interfaces and wallpapers, natural images, data visualizations, and games, together with changes in the wall observation. ReGLaR outperforms eighteen image restoration baselines across the five quantitative datasets and retains a comparative advantage across most observation variations. The recovered brightness, color distributions, regional layouts, and principal contours demonstrate the initial feasibility of hidden-screen recovery through an ordinary camera's view of wall reflections.