Old film restoration remains challenging because archival footage often combines scratches, blotches, flicker, blur, and temporal artifacts, disrupting the balance among structural recovery, regional texture repair, and fine artifact suppression. We propose Multi-Granularity Feature Decomposition with Ranking-Aware Regularization for Old Film Restoration (MgfrOFR), a multi-granularity extension of a Mamba-based old film restoration backbone. MgfrOFR first decomposes propagated features into coarse-to-fine restoration branches and feeds branch summaries back through channel-wise recalibration so different degradation scales can be represented without competing in a single feature stream. It then introduces a ranking-aware triplet regularizer that penalizes correlated temporal-similarity rankings across branches during training while preserving the baseline inference interface. Experiments on the Synthetic/Real-World Old Video (SRWOV) benchmark show statistically significant fidelity gains on synthetic clips, favorable no-reference naturalness on real-world archival footage, and a bounded perceptual-quality trade-off. These results indicate that representation-level branch balance is an effective inductive bias for old film restoration under heterogeneous and asymmetric degradation.

Old films suffer from scratches, dust, and brightness flicker caused by aging film stock and unstable analog exposure. Recurrent restoration frameworks suppress these artifacts under the guidance of degradation masks, yet pixel-domain frame differencing provides weak evidence for thin structural defects and confuses global brightness variation with content change. We present WaveletMask, a wavelet-domain degradation sensing framework that disentangles these two failure modes by construction: a high-frequency branch localizes transient structural defects from Haar detail-band differences between adjacent frames, a low-frequency branch isolates frame-level brightness deviations from coarse approximation responses, and a parameter-free maximum fusion rule passes the dominant cue to the recurrent gate. On the Synthetic and Real-World Old Video (SRWOV) benchmark, WaveletMask attains the best PSNR among ten re-trained methods (26.60 dB, $+0.61$ dB over the strongest competitor), and a paired comparison against the Recurrent Transformer Network (RTN) confirms a $+0.45$ dB gain while adding only 898 detector parameters. On real archival footage, WaveletMask removes scratches and flicker more cleanly while better preserving film texture and temporal stability. These results indicate that explicit wavelet-domain separation of structural and photometric cues offers a reliable, nearly cost-free upgrade for mask-guided recurrent restoration.