recurrent super-resolution
**Recurrent super-resolution** is the **video enhancement approach that propagates hidden features across timesteps so each output frame benefits from long temporal context** - it offers strong temporal continuity and efficient memory reuse for long sequences.
**What Is Recurrent SR?**
- **Definition**: VSR architecture with temporal state passed from frame to frame during inference.
- **State Role**: Encodes accumulated historical detail and motion context.
- **Direction Options**: Forward-only for streaming or bidirectional for offline quality.
- **Representative Models**: BasicVSR-style pipelines with flow-guided propagation.
**Why Recurrent SR Matters**
- **Long-Range Context**: Can integrate evidence across many frames beyond fixed windows.
- **Temporal Stability**: Recurrent propagation encourages coherent output trajectories.
- **Memory Efficiency**: Avoids storing full long-window feature sets explicitly.
- **Quality Potential**: Strong results when alignment and propagation are stable.
- **Streaming Suitability**: Natural fit for online enhancement workflows.
**Recurrent SR Components**
**Propagation Module**:
- Warp previous hidden features into current frame coordinates.
- Combine with current frame features for updated state.
**Bidirectional Fusion**:
- Offline variants run forward and backward passes.
- Merge both directions for higher quality.
**Reconstruction Head**:
- Convert propagated features into high-resolution frame output.
- Apply temporal consistency losses during training.
**How It Works**
**Step 1**:
- For each frame, align previous state using flow and update recurrent features with current observations.
**Step 2**:
- Decode enhanced frame from updated state and continue propagation through sequence.
Recurrent super-resolution is **a long-context VSR paradigm that leverages persistent temporal memory for coherent high-quality enhancement** - careful drift control is essential for stable performance over extended clips.