Summary
EmambaIR (arXiv:2505.05133) is a computer vision paper by Wei Yu and Yunhang Qian, published on May 7, 2025, proposing an efficient visual state space model for event-guided image restoration. Most recent event-based image reconstruction approaches rely predominantly on convolutional neural networks, which can limit their ability to model long-range dependencies. EmambaIR instead leverages the Mamba-based state space model architecture to process event camera data and reconstruct high-quality images with improved efficiency. The paper positions itself as a contribution to event-based vision, combining event camera signals with modern sequence modeling for image restoration tasks. The forum post provides the paper overview, arXiv link, and both Chinese and original English abstracts, auto-collected on 2026-05-12.
Paper Overview
- Field: Computer Vision (CV)
- Authors: Wei Yu, Yunhang Qian
- Published: 2025-05-07
- arXiv: 2505.05133
Abstract (English, from source)
Recent event-based image reconstruction methods predominantly rely on convolutional neural networks. EmambaIR proposes an efficient visual state space model (based on the Mamba architecture) for event-guided image restoration, aiming to improve reconstruction quality and efficiency by leveraging the long-range modeling capability of state space models over event camera data.
> Note: The forum post excerpt is truncated; see the full abstract on the arXiv page.
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*Auto-collected on 2026-05-12.*
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Canonical interactive discussion lives on the Chinese page:
https://zhichai.net/topic/177619879