StableFlow: Real-Time 4K Video Super-Resolution With Robust Feature Propagation

Real-time 4K video super-resolution (VSR) requires the effective reuse of temporal information under strict latency constraints, typically relying on temporal alignment and real-time reconstruction. However, imperfect alignment introduces perturbations into the temporal recursion, which can accumulate over time and degrade reconstruction quality-an effect widely observed in recurrent VSR pipelines but rarely analyzed explicitly from a dynamical perspective under real-time constraints. In this work, we formulate real-time VSR inference as a recurrent dynamical process, embedding alignment within the state update. This formulation enables explicit analysis of how alignment-induced perturbations are introduced and propagated during inference. Motivated by this analysis, we propose StableFlow, a stability-guided real-time VSR framework. Specifically, StableFlow introduces an Alignment Gain-aware Alignment Module (AGAM) to enhance the utility of temporally aligned features, a State-aware Perturbation Control Filter (SPCF) to suppress unreliable propagated information, and a Temporal Propagation Control (TPC) loss to regulate long-term recurrent state evolution. Our method combines efficient temporal alignment with state-aware perturbation control and a temporal propagation control loss to stabilize long-term recurrent behavior. Experiments demonstrate that StableFlow achieves real-time 4K performance (over 40 FPS on an NVIDIA RTX 3090) for $4\times $ upscaling from $960\times 540$ inputs to $3840\times 2160$ outputs, with only 321K parameters and 77.45G FLOPs, while maintaining competitive reconstruction quality.