Bayesian Based Unrolling for Reconstruction and Super-resolution of Single-Photon Lidar Systems

Research output: Contribution to conferencePaperpeer-review

23 Downloads (Pure)


Deploying 3D single-photon Lidar imaging in real world applications faces several challenges due to imaging in high noise environments and with sensors having limited resolution. This paper presents a deep learning algorithm based on unrolling a Bayesian model for the reconstruction and super-resolution of 3D single-photon Lidar. The resulting algorithm benefits from the advantages of both statistical and learning based frameworks, providing best estimates with improved network interpretability. Compared to existing learning-based solutions,the proposed architecture requires a reduced number of trainable parameters, is more robust to noise and mismodelling of the system impulse response function, and provides richer information about the estimates including uncertainty measures. Results on synthetic and real data show competitive results regarding the quality of the inference and computational complexity when compared to state-of-the-art algorithms.
Original languageEnglish
Publication statusPublished - 12 Jun 2023
EventInternational Symposium on Computational Sensing 2023 - Luxembourg, Luxembourg
Duration: 12 Jun 202314 Jun 2023


ConferenceInternational Symposium on Computational Sensing 2023
Internet address


  • eess.IV


Dive into the research topics of 'Bayesian Based Unrolling for Reconstruction and Super-resolution of Single-Photon Lidar Systems'. Together they form a unique fingerprint.

Cite this