CDC-VSR: Unifying Cross-Domain Continuity for High-Quality Video Super-Resolution

Video super-resolution (VSR) requires the coordinated modeling of spatial structures, temporal dynamics, and spectral characteristics. However, existing methods often treat these aspects separately. Spatially, uniform processing neglects the sparse distribution of high-frequency details, weakening the recovery of critical fine structures. Temporally, conventional feature aggregation accumulates appearance information without explicitly modeling the evolution of scene states across frames. These limitations are further exacerbated during optimization, where the objectives of detail restoration and temporal smoothness often induce conflicting gradients that hinder joint convergence. To address these issues, we propose CDC-VSR, a unified framework that enforces cross-domain continuity in representation learning, temporal propagation, and optimization. Specifically, we introduce a frequency-aware wavelet refinement module to selectively enhance structural components, a differential memory propagation mechanism to capture meaningful inter-frame transitions, and a conflict-aware gradient alignment strategy to reconcile reconstruction fidelity with temporal consistency. Extensive experiments show that CDC-VSR achieves superior reconstruction quality and temporal stability while maintaining a compact model size.