A Practical Gated Recurrent Transformer Network Incorporating Multiple Fusions for Video Denoising
This addresses the need for practical, low-latency denoising in real-time camera applications, representing an incremental improvement over existing multi-frame methods.
The paper tackles the problem of real-time video denoising by proposing a gated recurrent Transformer network (GRTN) that achieves state-of-the-art denoising performance with only a single-frame delay, comparable to methods with significant delays like 16 frames.
State-of-the-art (SOTA) video denoising methods employ multi-frame simultaneous denoising mechanisms, resulting in significant delays (e.g., 16 frames), making them impractical for real-time cameras. To overcome this limitation, we propose a multi-fusion gated recurrent Transformer network (GRTN) that achieves SOTA denoising performance with only a single-frame delay. Specifically, the spatial denoising module extracts features from the current frame, while the reset gate selects relevant information from the previous frame and fuses it with current frame features via the temporal denoising module. The update gate then further blends this result with the previous frame features, and the reconstruction module integrates it with the current frame. To robustly compute attention for noisy features, we propose a residual simplified Swin Transformer with Euclidean distance (RSSTE) in the spatial and temporal denoising modules. Comparative objective and subjective results show that our GRTN achieves denoising performance comparable to SOTA multi-frame delay networks, with only a single-frame delay.