# Bilevel Inverse Problems in Neuromorphic Imaging

arXiv: https://arxiv.org/abs/2302.02052 · HTML: https://mlsanity.com/paper/2302.02052/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2023.

## Built on by (most cited first, 4 of 4)

- [Dynamic Reconstruction from Neuromorphic Data](https://mlsanity.com/paper/2408.15465/) (2024)
- [Optical Linear Systems Framework for Event Sensing and Computational Neuromorphic Imaging](https://mlsanity.com/paper/2601.13498/) (2026)
- [GNEP Based Dynamic Segmentation and Motion Estimation for Neuromorphic Imaging](https://mlsanity.com/paper/2307.02595/) (2023)
- [ESAR: Event-Based Synthetic Aperture Reconstruction](https://mlsanity.com/paper/2607.15073/) (2026)

## Abstract (arXiv)

Event or Neuromorphic cameras are novel biologically inspired sensors that record data based on the change in light intensity at each pixel asynchronously. They have a temporal resolution of microseconds. This is useful for scenes with fast moving objects that can cause motion blur in traditional cameras, which record the average light intensity over an exposure time for each pixel synchronously. This paper presents a bilevel inverse problem framework for neuromorphic imaging. Existence of solution to the inverse problem is established. Second order sufficient conditions are derived under special situations for this nonconvex problem. A second order Newton type solver is derived to solve the problem. The efficacy of the approach is shown on several examples.