Progressive Light-Confidence Representation Learning for Hidden Screen Reconstruction from Wall Light Patterns

Can an ordinary camera recover what a hidden screen displays by observing only the light it casts onto a wall? The task is governed by two coupled difficulties: (i) screen emission, ambient illumination, secondary transport, wall texture, and sensor noise mix in the wall pattern, so screen-consistent evidence varies across space; (ii) diffuse propagation attenuates high-frequency structure, leaving fine detail weakly represented and increasing reconstruction ambiguity. We introduce Progressive Light-Confidence Representation Learning (PLRL), which addresses these difficulties through ordered restoration and confidence-calibrated detail recovery. Its Physics-Guided Progressive Learning (PPRL) organizes wall evidence into coarse, intermediate, and fine scales and reconstructs layout, structure, and detail through bounded residual updates. Its Light-Propagation Confidence Representation (LPCR) combines scale compatibility, edge state, and image-wide context to identify reliable detail and calibrate its contribution. Across four screen-content domains, PLRL recovers the hidden display's brightness distribution, regional layout, and major contours, achieving the lowest LPIPS on every domain when compared with 24 restoration methods. Experiments spanning illumination, wall response, distance, and viewing changes establish the initial feasibility of recovering principal screen content from wall light captured by an ordinary camera.