# Neuromorphic Synergy for Video Binarization

arXiv: https://arxiv.org/abs/2402.12644 · HTML: https://mlsanity.com/paper/2402.12644/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 68 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Builds on (most cited first, 22 of 22)

- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal](https://mlsanity.com/paper/1503.00593/) (2015)
- [Asynchronous, Photometric Feature Tracking using Events and Frames](https://mlsanity.com/paper/1807.09713/) (2018)
- [Event-Based Fusion for Motion Deblurring with Cross-modal Attention](https://mlsanity.com/paper/2112.00167/) (2021)
- [Learning Event-Based Motion Deblurring](https://mlsanity.com/paper/2004.05794/) (2020)
- [Event Enhanced High-Quality Image Recovery](https://mlsanity.com/paper/2007.08336/) (2020)
- [Learning Dense and Continuous Optical Flow from an Event Camera](https://mlsanity.com/paper/2211.09078/) (2022)
- [Unifying Motion Deblurring and Frame Interpolation with Events](https://mlsanity.com/paper/2203.12178/) (2022)
- [Learning to Extract a Video Sequence from a Single Motion-Blurred Image](https://mlsanity.com/paper/1804.04065/) (2018)
- [Motion Deblurring with Real Events](https://mlsanity.com/paper/2109.13695/) (2021)
- [Bringing Alive Blurred Moments](https://mlsanity.com/paper/1804.02913/) (2018)
- [An Asynchronous Kalman Filter for Hybrid Event Cameras](https://mlsanity.com/paper/2012.05590/) (2020)
- [Event Camera Calibration of Per-pixel Biased Contrast Threshold](https://mlsanity.com/paper/2012.09378/) (2020)
- [A selectional auto-encoder approach for document image binarization](https://mlsanity.com/paper/1706.10241/) (2017)
- [Learning to Deblur and Generate High Frame Rate Video with an Event Camera](https://mlsanity.com/paper/2003.00847/) (2020)
- [Feedback control of event cameras](https://mlsanity.com/paper/2105.00409/) (2021)
- [Learning to Super-Resolve Blurry Images with Events](https://mlsanity.com/paper/2302.13766/) (2023)
- [TagSLAM: Robust SLAM with Fiducial Markers](https://mlsanity.com/paper/1910.00679/) (2019)
- [Autofocus for Event Cameras](https://mlsanity.com/paper/2203.12321/) (2022)
- [Event-Based high-speed low-latency fiducial marker tracking](https://mlsanity.com/paper/2110.05819/) (2021)
- [A Regularization Approach to Blind Deblurring and Denoising of QR Barcodes](https://mlsanity.com/paper/1410.6333/) (2014)
- [Detection of Binary Square Fiducial Markers Using an Event Camera](https://mlsanity.com/paper/2012.06516/) (2020)

## Built on by (most cited first, 3 of 3)

- [LAND: A Longitudinal Analysis of Neuromorphic Datasets](https://mlsanity.com/paper/2602.15973/) (2026)
- [EROAM: Event-based Camera Rotational Odometry and Mapping in Real-time](https://mlsanity.com/paper/2411.11004/) (2024)
- [See Silhouettes in Motion with Neuromorphic Vision](https://mlsanity.com/paper/2605.17984/) (2026)

## Abstract (arXiv)

Bimodal objects, such as the checkerboard pattern used in camera calibration, markers for object tracking, and text on road signs, to name a few, are prevalent in our daily lives and serve as a visual form to embed information that can be easily recognized by vision systems. While binarization from intensity images is crucial for extracting the embedded information in the bimodal objects, few previous works consider the task of binarization of blurry images due to the relative motion between the vision sensor and the environment. The blurry images can result in a loss in the binarization quality and thus degrade the downstream applications where the vision system is in motion. Recently, neuromorphic cameras offer new capabilities for alleviating motion blur, but it is non-trivial to first deblur and then binarize the images in a real-time manner. In this work, we propose an event-based binary reconstruction method that leverages the prior knowledge of the bimodal target's properties to perform inference independently in both event space and image space and merge the results from both domains to generate a sharp binary image. We also develop an efficient integration method to propagate this binary image to high frame rate binary video. Finally, we develop a novel method to naturally fuse events and images for unsupervised threshold identification. The proposed method is evaluated in publicly available and our collected data sequence, and shows the proposed method can outperform the SOTA methods to generate high frame rate binary video in real-time on CPU-only devices.