MCRRL: Recovering Hidden Screen Content from Diffuse Wall Reflections

We ask: when a camera cannot directly capture a screen, can the screen content be recovered using only an image of the light cast by the screen onto a wall? With the display outside the camera view, diffuse reflection spreads and mixes screen light, weakening local correspondence and attenuating the directional evidence needed for small text, thin lines, and interface structure. Direct residual regression can also average foreground with background or introduce texture where the measurement is ambiguous. We therefore propose Measurement-Conditioned Raster Representation Learning (MCRRL) to learn structural correspondence between weak wall-light evidence and the hidden screen. Measurement-Conditioned Proximal Raster Fusion (MPRF) organizes directional cues through an orientation-masked dictionary and finite proximal updates, learning structural correspondence across scales. Evidence-Calibrated Digital Raster Composition Reconstruction (DRCR) uses the resulting decoder features to form latent foreground, background, and opacity fields, then uses an evidence-conditioned gate and clipping to refine their composition. Together, the two stages organize the surviving screen cues and control how they contribute to the reconstruction. Experiments on four real paired datasets cover interfaces, charts, dense glyph patterns, and browser layouts. MCRRL outperforms the compared reconstruction methods in PSNR, SSIM, and LPIPS across all four datasets. The recovered images reveal brightness distributions, regional layouts, principal contours, and portions of the displayed content. These results establish the preliminary feasibility of recovering hidden screen information from diffuse wall light with an ordinary RGB camera.