mlsanityData to 7 Oct 2026

Quantifying Noise of Dynamic Vision Sensor

arXiv 2404.01948 ↗First posted 2 Apr 2024cs.CVcited by 1

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Abstract (arXiv)

Dynamic visual sensors (DVS) are characterized by a large amount of background activity (BA) noise, which it is mixed with the original (cleaned) sensor signal. The dynamic nature of the signal and the absence in practical application of the ground truth, it clearly makes difficult to distinguish between noise and the cleaned sensor signals using standard image processing techniques. In this letter, a new technique is presented to characterise BA noise derived from the Detrended Fluctuation Analysis (DFA). The proposed technique can be used to address an existing DVS issues, which is how to quantitatively characterised noise and signal without ground truth, and how to derive an optimal denoising filter parameters. The solution of the latter problem is demonstrated for the popular real moving-car dataset.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 31 references that are in the map. First posted on arXiv 2 Apr 2024.

Builds on

10 of 31 references.

References · 10

  1. 2019Event-based Vision: A Surveycited by 1,010
  2. 2018A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimationcited by 178
  3. 2022Secrets of Event-Based Optical Flowcited by 92
  4. 2019Focus Is All You Need: Loss Functions For Event-based Visioncited by 83
  5. 2021How to Calibrate Your Event Cameracited by 53
  6. 2020Event Probability Mask (EPM) and Event Denoising Convolutional Neural Network (EDnCNN) for Neuromorphic Camerascited by 38
  7. 2023E-MLB: Multilevel Benchmark for Event-Based Camera Denoisingcited by 27
  8. 2023Shining light on the DVS pixel: A tutorial and discussion about biasing and optimizationcited by 21
  9. 2020Inceptive Event Time-Surfaces for Object Classification Using Neuromorphic Camerascited by 19
  10. 2023Optimal biasing and physical limits of DVS event noisecited by 13

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025On the Theory of Bias Tuning in Event Camerascited by 1