A Practical Gated Recurrent Transformer Network Incorporating Multiple Fusions for Video Denoising
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.
Code (0)
등록된 구현이 없습니다.
Tasks
DenoisingVideo DenoisingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Recurrent Graph Transformer Network for Multiple Fault Localization in Naval Shipboard Systems
The integration of power electronics building blocks in modern MVDC 12kV Naval ship systems enhances energy management and functionality but also introduces complex fault detection and control challenges. These challenge…
Diagnosticenergy managementFault DetectionFault Diagnosis+3Gated Slot Attention for Efficient Linear-Time Sequence Modeling
Linear attention Transformers and their gated variants, celebrated for enabling parallel training and efficient recurrent inference, still fall short in recall-intensive tasks compared to traditional Transformers and dem…
Universal In-Context Approximation By Prompting Fully Recurrent Models
Zero-shot and in-context learning enable solving tasks without model fine-tuning, making them essential for developing generative model solutions. Therefore, it is crucial to understand whether a pretrained model can be …
In-Context LearningMambaLiger: Linearizing Large Language Models to Gated Recurrent Structures
Transformers with linear recurrent modeling offer linear-time training and constant-memory inference. Despite their demonstrated efficiency and performance, pretraining such non-standard architectures from scratch remain…
Gated recurrent units and temporal convolutional network for multilabel classification
Multilabel learning tackles the problem of associating a sample with multiple class labels. This work proposes a new ensemble method for managing multilabel classification: the core of the proposed approach combines a se…
Classification