Action-Regulated State Spaces for All-in-One Endoscopic Image Restoration

All-in-one medical image restoration faces a fundamental spatial action-allocation problem: degradations vary widely in location, extent, and severity. We identify a previously overlooked failure mode of state-space restorers under such spatial heterogeneity. Because corrective evidence is recurrently written into and propagated through shared state dynamics, an action appropriate for one region may cross its intended boundary and inadvertently alter intact tissue. We term this phenomenon restoration-action spillover. To address this issue, we proppse BARS-MambaIR, a blind action-regulated state-space model that predicts dense demand, scope, and magnitude fields to encode where, how far, and how strongly restoration should act. These fields regulate state writing, propagation decay, boundary-conditioned attenuation, and local-to-global action routing, while also gating the output residual. An action-allocation consistency objective learns these fields directly from paired supervision, without requiring degradation labels or region annotations, to suppress intact-region distortion and restoration spillover. Controlled footprint–severity sweeps, localized state interventions, and field-swapping experiments further reveal how spatially regulated state propagation limits restoration-action spillover.