Paper Info
Title
Non-Local Restoration Of Sparse 3d Single-Photon Data
Abstract
This paper presents a new algorithm for the non-local restoration of single-photon 3-Dimensional Lidar images acquired in the photon starved regime or with a reduced number of scanned spatial points (pixels). The algorithm alternates between two steps: evaluation of the spatial correlations between pixels using a graph, then restore the depth and reflectivity images by their spatial correlations. To reduce the computational cost associated with the graph, we adopt a non-uniform sampling approach, where bigger patches are assigned to homogeneous regions and smaller ones to heterogeneous regions. The restoration of 3D images is achieved by minimizing a cost function accounting for the data Poisson statistics and the non-local spatial correlations between patches. This minimization problem is efficiently solved using the alternating direction method of multipliers (ADMM) that presents fast convergence properties. Results on real Lidar data show the benefits of the proposed algorithm in improving the quality of the estimated depth images, especially in photon starved cases, which can contain a reduced number of photons.
Year
DOI
Venue
2019
10.23919/EUSIPCO.2019.8902525
2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO)
Keywords
Field
DocType
3D Lidar imaging, image restoration, Poisson statistics, graph, non-uniform sampling
Convergence (routing),Photon,Computer science,Algorithm,Lidar,Sampling (statistics),Pixel,Poisson distribution,Image restoration,Nonuniform sampling
Conference
ISSN
Citations 
PageRank 
2076-1465
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Songmao Chen101.01
Abderrahim Halimi229220.72
Ximing Ren3123.14
Aongus McCarthy4215.42
XiuQin Su500.68
Gerald S. Buller6318.26
Stephen McLaughlin716816.62