AdaBridge-SR: Adaptive Bridge Matching for Real-World Image Super-Resolution

Despite their remarkable performance in real-world imagesuper-resolution, diffusion models remain challenged by the perception–distortion trade-off between structural fidelity and perceptual realism.Existing methods typically rely on a globally predefined restoration tra-jectory, applying spatially uniform noise and a fixed temporal schedule.Such rigid trajectories often lead to a dilemma: reliable structures may bedistorted, while severely degraded textures may become over-smoothed.To address this, we propose AdaBridge-SR, built upon a novel spatio-temporal adaptive bridge matching (ST-ABM) formulation that castsrestoration as a controlled bridge between degraded and clean imagedistributions. ST-ABM decouples the bridge stochasticity into spatialand temporal controls, adaptively determining where to inject stochas-ticity and when to allocate generative capacity conditioned on the inputdegradation. We instantiate these controls with a lightweight AdaptiveBridge Controller that predicts a spatial stochasticity map and a timereparameterization. Consequently, AdaBridge-SR unifies deterministicrestoration and stochastic exploration within a single model. Extensiveexperiments demonstrate that it achieves a superior perception–distortionbalance, supporting efficient deterministic one-step inference and high-quality stochastic few-step bridge refinement. Our code and models areavailable at https://github.com/W-JG/AdaBridge-SR.