# Filter-Based Reconstruction of Images from Events

arXiv: https://arxiv.org/abs/2510.20071 · HTML: https://mlsanity.com/paper/2510.20071/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 29 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Builds on (most cited first, 18 of 18)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy](https://mlsanity.com/paper/2009.08283/) (2020)
- [Event-based High Dynamic Range Image and Very High Frame Rate Video Generation using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1811.08230/) (2018)
- [Efficient Neuromorphic Signal Processing with Loihi 2](https://mlsanity.com/paper/2111.03746/) (2021)
- [Continuous-time Intensity Estimation Using Event Cameras](https://mlsanity.com/paper/1811.00386/) (2018)
- [How to Calibrate Your Event Camera](https://mlsanity.com/paper/2105.12362/) (2021)
- [Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation](https://mlsanity.com/paper/1607.06283/) (2016)
- [Speed Invariant Time Surface for Learning to Detect Corner Points with Event-Based Cameras](https://mlsanity.com/paper/1903.11332/) (2019)
- [HyperE2VID: Improving Event-Based Video Reconstruction via Hypernetworks](https://mlsanity.com/paper/2305.06382/) (2023)
- [Event Camera Calibration of Per-pixel Biased Contrast Threshold](https://mlsanity.com/paper/2012.09378/) (2020)
- [Shining light on the DVS pixel: A tutorial and discussion about biasing and optimization](https://mlsanity.com/paper/2304.04706/) (2023)
- [Asynchronous Spatial Image Convolutions for Event Cameras](https://mlsanity.com/paper/1812.00438/) (2018)
- [EventHDR: from Event to High-Speed HDR Videos and Beyond](https://mlsanity.com/paper/2409.17029/) (2024)
- [Frequency Cam: Imaging Periodic Signals in Real-Time](https://mlsanity.com/paper/2211.00198/) (2022)
- [E-Calib: A Fast, Robust and Accurate Calibration Toolbox for Event Cameras](https://mlsanity.com/paper/2306.09078/) (2023)
- [Fast Event-based Double Integral for Real-time Robotics](https://mlsanity.com/paper/2305.05925/) (2023)
- [An Event-based Fast Intensity Reconstruction Scheme for UAV Real-time Perception](https://mlsanity.com/paper/2508.02238/) (2025)

## Abstract (arXiv)

Reconstructing an intensity image from the events of a moving event camera is a challenging task that is typically approached with neural networks deployed on graphics processing units. This paper presents a much simpler, FIlter Based Asynchronous Reconstruction method (FIBAR). First, intensity changes signaled by events are integrated with a temporal digital IIR filter. To reduce reconstruction noise, stale pixels are detected by a novel algorithm that regulates a window of recently updated pixels. Arguing that for a moving camera, the absence of events at a pixel location likely implies a low image gradient, stale pixels are then blurred with a Gaussian filter. In contrast to most existing methods, FIBAR is asynchronous and permits image read-out at an arbitrary time. It runs on a modern laptop CPU at about 42(140) million events/s with (without) spatial filtering enabled. A few simple qualitative experiments are presented that show the difference in image reconstruction between FIBAR and a neural network-based approach (FireNet). FIBAR's reconstruction is noisier than neural network-based methods and suffers from ghost images. However, it is sufficient for certain tasks such as the detection of fiducial markers. Code is available at https://github.com/ros-event-camera/event_image_reconstruction_fibar