multi-frame super-resolution

**Multi-frame super-resolution** is the **VSR strategy that uses a fixed temporal window around a target frame to reconstruct higher-resolution output through aligned evidence fusion** - it balances temporal context and parallel processing efficiency. **What Is Multi-Frame SR?** - **Definition**: Super-resolution using several neighboring frames, often symmetric around the center frame. - **Window Size**: Typical settings use 3, 5, or 7 frames depending on compute budget. - **Alignment Requirement**: Neighbor frames must be motion-aligned before fusion. - **Output Mode**: Usually center-frame enhancement, optionally repeated in sliding fashion. **Why Multi-Frame SR Matters** - **Detail Gain**: More temporal evidence improves reconstruction of fine textures. - **Robustness**: Window context reduces effect of transient noise and blur in any single frame. - **Parallelism**: Windowed design allows batch processing and lower latency than full recurrence. - **Engineering Simplicity**: Easier deployment than long-state recurrent systems. - **Strong Baseline**: Widely used in practical restoration products. **Model Components** **Alignment Module**: - Flow-based or deformable alignment to reference frame. - Multi-scale alignment often improves large-motion cases. **Fusion Module**: - Attention or weighted blending of aligned features. - Learns confidence-aware temporal aggregation. **Reconstruction Module**: - Upsampling layers produce high-resolution output. - Losses include pixel, perceptual, and temporal terms. **How It Works** **Step 1**: - Gather fixed frame window, extract features, and align all neighbor features to center frame. **Step 2**: - Fuse aligned features and reconstruct high-resolution center frame. Multi-frame super-resolution is **a practical temporal fusion approach that captures most VSR benefits with predictable compute and latency** - it remains a preferred choice for many production enhancement pipelines.