Recoverable Evidence in Wall Reflections: Structure-Protected Degradation Representation Learning for Screen Content Recovery

Can an ordinary camera recover a hidden screen when it sees only the screen's blurry reflection on a wall? We establish the initial feasibility of this task and recover the principal screen content and structure from diffuse wall observations. The core challenge is that spatial propagation and surface reflection weaken text, boundaries, and color while mixing genuine screen structure with wall texture, illumination, reflections, and sensor noise. Moreover, each local region carries only weak evidence, making both the need for correction and its appropriate magnitude difficult to determine. We propose Structure-Protected Degradation Representation Learning (SPDRL) to distinguish reliable screen structure from wall-induced interference. SPDRL combines the wall observation, a frozen base reconstruction, their discrepancy, and local cues into a shared representation with structure and degradation readouts. Representation-Calibrated Correction Admission (RCCA) then uses this representation and correction cues to decide where and how strongly to refine the reconstruction. This structure-protected, spatially calibrated recovery preserves reliable content while strengthening weakly supported regions. Across four screen-content types, the complete method leads all 12 main benchmark comparisons against 21 restoration baselines. Experiments spanning illumination, distance, viewpoint, blur, distortion, compression, and occlusion further demonstrate recovery across varied imaging conditions. Together, these results establish the initial feasibility of recovering the principal content and structure of a hidden screen from wall reflections captured by an ordinary camera.