# Near-infrared Image Deblurring and Event Denoising with Synergistic Neuromorphic Imaging

arXiv: https://arxiv.org/abs/2503.01193 · HTML: https://mlsanity.com/paper/2503.01193/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 54 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Scale-recurrent Network for Deep Image Deblurring](https://mlsanity.com/paper/1802.01770/) (2018)
- [Low-Light Image and Video Enhancement Using Deep Learning: A Survey](https://mlsanity.com/paper/2104.10729/) (2021)
- [Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera](https://mlsanity.com/paper/1811.10180/) (2018)
- [Event-Based Fusion for Motion Deblurring with Cross-modal Attention](https://mlsanity.com/paper/2112.00167/) (2021)
- [LEDNet: Joint Low-light Enhancement and Deblurring in the Dark](https://mlsanity.com/paper/2202.03373/) (2022)
- [Learning Event-Based Motion Deblurring](https://mlsanity.com/paper/2004.05794/) (2020)
- [Intriguing Findings of Frequency Selection for Image Deblurring](https://mlsanity.com/paper/2111.11745/) (2021)
- [Multiscale Structure Guided Diffusion for Image Deblurring](https://mlsanity.com/paper/2212.01789/) (2022)
- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation Dataset](https://mlsanity.com/paper/1908.00682/) (2019)
- [Motion Deblurring with Real Events](https://mlsanity.com/paper/2109.13695/) (2021)
- [Event Probability Mask (EPM) and Event Denoising Convolutional Neural Network (EDnCNN) for Neuromorphic Cameras](https://mlsanity.com/paper/2003.08282/) (2020)
- [Efficient Multi-scale Network with Learnable Discrete Wavelet Transform for Blind Motion Deblurring](https://mlsanity.com/paper/2401.00027/) (2023)
- [Motion-adaptive Separable Collaborative Filters for Blind Motion Deblurring](https://mlsanity.com/paper/2404.13153/) (2024)
- [DarkVisionNet: Low-Light Imaging via RGB-NIR Fusion with Deep Inconsistency Prior](https://mlsanity.com/paper/2303.06834/) (2023)
- [LED: A Large-scale Real-world Paired Dataset for Event Camera Denoising](https://mlsanity.com/paper/2405.19718/) (2024)
- [Neuromorphic Imaging with Joint Image Deblurring and Event Denoising](https://mlsanity.com/paper/2309.16106/) (2023)
- [A Dual Sensor Computational Camera for High Quality Dark Videography](https://mlsanity.com/paper/2204.04987/) (2022)
- [CrossZoom: Simultaneously Motion Deblurring and Event Super-Resolving](https://mlsanity.com/paper/2309.16949/) (2023)
- [NIR-Assisted Image Denoising: A Selective Fusion Approach and A Real-World Benchmark Dataset](https://mlsanity.com/paper/2404.08514/) (2024)

## Built on by (most cited first, 1 of 1)

- [Histogram Assisted Quality Aware Generative Model for Resolution Invariant NIR Image Colorization](https://mlsanity.com/paper/2601.01103/) (2026)

## Abstract (arXiv)

The fields of imaging in the nighttime dynamic and other extremely dark conditions have seen impressive and transformative advancements in recent years, partly driven by the rise of novel sensing approaches, e.g., near-infrared (NIR) cameras with high sensitivity and event cameras with minimal blur. However, inappropriate exposure ratios of near-infrared cameras make them susceptible to distortion and blur. Event cameras are also highly sensitive to weak signals at night yet prone to interference, often generating substantial noise and significantly degrading observations and analysis. Herein, we develop a new framework for low-light imaging combined with NIR imaging and event-based techniques, named synergistic neuromorphic imaging, which can jointly achieve NIR image deblurring and event denoising. Harnessing cross-modal features of NIR images and visible events via spectral consistency and higher-order interaction, the NIR images and events are simultaneously fused, enhanced, and bootstrapped. Experiments on real and realistically simulated sequences demonstrate the effectiveness of our method and indicate better accuracy and robustness than other methods in practical scenarios. This study gives impetus to enhance both NIR images and events, which paves the way for high-fidelity low-light imaging and neuromorphic reasoning.