RefPLC: Latent Recovery and State-Conditioned Pixel Modulation for Reference-Based Super-Resolution

Severe degradation removes the very cues needed to identify missing image detail. A sharp reference can supply related evidence, but differences in pose, viewpoint, and appearance make direct transfer unreliable. Reference-guided generation must therefore address two linked questions: how to turn an unaligned observation into a target-compatible condition, and how to determine its additional contribution as reconstruction evolves. We propose RefPLC, which treats reference guidance as bounded restoration increments at the latent-condition and RGB-velocity interfaces of pixel diffusion. Restoration-Oriented Latent Transport (ROLT) aligns detail-sensitive VAE differences and interprets them relative to the degraded target, preserving the LQ latent as the condition's residual base. State-Conditioned Velocity Modulation (SCVM) compares shared-decoder responses with and without reference evidence at the same sampling state, then spatially modulates the resulting correction. The two stages connect reference preparation to its use within one restoration trajectory. On Single717, RefPLC achieves 25.81 dB PSNR and 67.89 MUSIQ, improving over the equally adapted PiD baseline by 1.89 dB and 4.02, respectively. External evaluation yields 45.88 FID on WR-SR and 28.17 dB PSNR on Face. In a paired 90-target study, Top references improve PSNR by 0.761 dB over the same trained model without a reference. These results support separating target-conditioned evidence recovery from state-dependent reference application.