RECALL: Learning Content-Anchored Spectral Representation via Restoration-as-Allocation for Hidden Screen Reconstruction

Can an ordinary camera positioned behind a wall-facing display recover its con-tent from diffuse wall light alone? Propagation, scattering, occlusion, blur, andsensor integration disperse weak content cues, disrupting local screen-to-wallcorrespondence and making appearance-based capability allocation ambiguous.We present REpresentation-enabled Content-Anchored aLLocation Learning (RE-CALL), a wall-image-only framework built around Content-Anchored SpectralRepresentation (CASR) and Restoration-as-Allocation Learning (RAL). CASRorganizes patchwise spectral evidence around a sample-dependent content anchorderived from the wall image with a frozen visual encoder, combining smooth-layout and residual-detail cues. RAL learns soft spatial allocation by comparingthe pooled errors of complete reconstructions produced under local and context-augmented capability choices on the same wall observation during training. Acrossseven screen-content categories, RECALL achieves the strongest overall screen-reconstruction performance among the compared image-restoration methods, withthe highest PSNR and SSIM in every category and the lowest LPIPS in five. Acrossvaried capture conditions and screen-content scenes, it coarsely recovers hidden-screen color and brightness distributions, regional layouts, and principal contoursfrom blurred wall-light patches. These experiments demonstrate the feasibility ofobserving hidden screen content with an ordinary camera capturing only wall lightpatches.