World-Model-Inspired Flicker State Modeling for Burst Flicker Removal

Burst imaging under unstable illumination suffers from flicker, a periodic degradation that modulates local brightness differently across adjacent frames, creating frame-specific flicker states. Existing methods rely on spatial priors or reference-centric fusion without explicitly modeling the evolving flicker state, whose temporal periodicity makes it predictable from adjacent observations by world models that capture latent state transitions. Motivated by this predictive capacity, we propose StateFlicker, a state-aware burst restoration framework that interprets adjacent frames as observations under evolving flicker states. StateFlicker comprises two state-aware designs: Cross-State Visibility (CSV) and Flicker-State Understanding (FSU). CSV leverages complementary visibility across adjacent flicker states to selectively recover content suppressed under the center state. With this cross-state support, FSU constructs dual state pathways through a frozen world model to obtain predicted and observed center states, and distills their state discrepancy into dense features that characterize the center-specific flicker condition for restoration. Extensive experiments on the burst flicker removal dataset show that StateFlicker achieves superior performance over state-of-the-art methods.