Restoration of multilayered single-photon 3D Lidar images

This paper presents a new algorithm for the restoration of multilayered three-dimensional laser detection and ranging (3D Lidar) images. For multilayered targets such as semitransparent surfaces or when the transmitted light of the laser beam is incident on multiple surfaces at different depths, the returned signal may contain multiple peaks. Considering the Poisson statistics of these observations leads to a convex data fidelity term that is regularized using appropriate functions accounting for the spatial correlation between pixels and the sparse depth repartition of targets. More precisely, the spatial correlation is introduced using a convex total variation (TV) regularizer, and a collaborative sparse prior is used to introduce the depth prior knowledge. The resulting minimization problem is solved using the alternating direction method of multipliers (ADMM) that offers good convergence properties. The algorithm was validated using field data representing a man standing 1 meter behind camouflage, at an approximate stand-off distance of 230m from the system. The results show the benefit of the proposed strategy in that it improves the quality of the imaged objects at different depths and under reduced acquisition times.

• Publication year

  2017

• Venue

  European Signal Processing Conference

• Publication date

  2017-08-01

• Fields of study

  Physics, Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.23919/EUSIPCO.2017.8081299
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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