EmambaIR (arXiv:2505.05133) is a computer vision paper by Wei Yu and Yunhang Qian, published on May 7, 2025, introducing an efficient visual state space model for event-guided image reconstruction. While most recent event-based image reconstruction methods rely on convolutional neural networks, this work leverages a Mamba-style state space architecture to process event camera data and reconstruct images with improved efficiency. The paper is relevant to researchers working on event cameras, neuromorphic vision, image restoration, and efficient deep learning architectures, offering an alternative to CNN- and Transformer-based approaches for reconstructing high-quality images from event streams.
Recent event-based image reconstruction methods predominantly rely on convolutional neural networks. EmambaIR proposes an efficient visual state space model for event-guided image reconstruction, applying Mamba-style state space modeling to event camera data.
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