Restoration of Depth and Intensity Images using a Graph Laplacian Regularization

Abderrahim Halimi, Peter Connolly, Ximing Ren, Yoann Altmann, Istvan Gyongy, Robert K. Henderson, Stephen McLaughlin, Gerald Stuart Buller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a new algorithm for the joint restoration of depth and intensity (DI) images constructed using a gated SPAD-array imaging system. The three dimensional (3D) data consists of two spatial dimensions and one temporal dimension, and contains photon counts (i.e., histograms). The algorithm is based on two steps: (i) construction of a graph connecting patches of pixels with similar temporal responses, and (ii) estimation of the DI values for pixels belonging to homogeneous spatial classes. The first step is achieved by building a graph representation of the 3D data, while giving a special attention to the computational complexity of the algorithm.
The second step is achieved using a Fisher scoring gradient descent algorithm while accounting for the data statistics and the Laplacian regularization term. Results on laboratory data show the benefit of the proposed strategy that improves the quality of the estimated DI images.
Original languageEnglish
Title of host publication2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
PublisherIEEE
ISBN (Electronic)9781538612514
DOIs
Publication statusPublished - 12 Mar 2018
Event7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Curacao, Netherlands
Duration: 10 Dec 201713 Dec 2017
http://www.cs.huji.ac.il/conferences/CAMSAP17/

Conference

Conference7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Abbreviated titleCAMSAP 2017
CountryNetherlands
CityCuracao
Period10/12/1713/12/17
Internet address

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pixel
histogram
restoration
statistics
laboratory

Cite this

Halimi, A., Connolly, P., Ren, X., Altmann, Y., Gyongy, I., Henderson, R. K., ... Buller, G. S. (2018). Restoration of Depth and Intensity Images using a Graph Laplacian Regularization. In 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) [8313136] IEEE. https://doi.org/10.1109/CAMSAP.2017.8313136
Halimi, Abderrahim ; Connolly, Peter ; Ren, Ximing ; Altmann, Yoann ; Gyongy, Istvan ; Henderson, Robert K. ; McLaughlin, Stephen ; Buller, Gerald Stuart. / Restoration of Depth and Intensity Images using a Graph Laplacian Regularization. 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2018.
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title = "Restoration of Depth and Intensity Images using a Graph Laplacian Regularization",
abstract = "This paper presents a new algorithm for the joint restoration of depth and intensity (DI) images constructed using a gated SPAD-array imaging system. The three dimensional (3D) data consists of two spatial dimensions and one temporal dimension, and contains photon counts (i.e., histograms). The algorithm is based on two steps: (i) construction of a graph connecting patches of pixels with similar temporal responses, and (ii) estimation of the DI values for pixels belonging to homogeneous spatial classes. The first step is achieved by building a graph representation of the 3D data, while giving a special attention to the computational complexity of the algorithm. The second step is achieved using a Fisher scoring gradient descent algorithm while accounting for the data statistics and the Laplacian regularization term. Results on laboratory data show the benefit of the proposed strategy that improves the quality of the estimated DI images.",
author = "Abderrahim Halimi and Peter Connolly and Ximing Ren and Yoann Altmann and Istvan Gyongy and Henderson, {Robert K.} and Stephen McLaughlin and Buller, {Gerald Stuart}",
year = "2018",
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Halimi, A, Connolly, P, Ren, X, Altmann, Y, Gyongy, I, Henderson, RK, McLaughlin, S & Buller, GS 2018, Restoration of Depth and Intensity Images using a Graph Laplacian Regularization. in 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)., 8313136, IEEE, 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Curacao, Netherlands, 10/12/17. https://doi.org/10.1109/CAMSAP.2017.8313136

Restoration of Depth and Intensity Images using a Graph Laplacian Regularization. / Halimi, Abderrahim; Connolly, Peter; Ren, Ximing; Altmann, Yoann; Gyongy, Istvan; Henderson, Robert K.; McLaughlin, Stephen; Buller, Gerald Stuart.

2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2018. 8313136.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Restoration of Depth and Intensity Images using a Graph Laplacian Regularization

AU - Halimi, Abderrahim

AU - Connolly, Peter

AU - Ren, Ximing

AU - Altmann, Yoann

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AU - Henderson, Robert K.

AU - McLaughlin, Stephen

AU - Buller, Gerald Stuart

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N2 - This paper presents a new algorithm for the joint restoration of depth and intensity (DI) images constructed using a gated SPAD-array imaging system. The three dimensional (3D) data consists of two spatial dimensions and one temporal dimension, and contains photon counts (i.e., histograms). The algorithm is based on two steps: (i) construction of a graph connecting patches of pixels with similar temporal responses, and (ii) estimation of the DI values for pixels belonging to homogeneous spatial classes. The first step is achieved by building a graph representation of the 3D data, while giving a special attention to the computational complexity of the algorithm. The second step is achieved using a Fisher scoring gradient descent algorithm while accounting for the data statistics and the Laplacian regularization term. Results on laboratory data show the benefit of the proposed strategy that improves the quality of the estimated DI images.

AB - This paper presents a new algorithm for the joint restoration of depth and intensity (DI) images constructed using a gated SPAD-array imaging system. The three dimensional (3D) data consists of two spatial dimensions and one temporal dimension, and contains photon counts (i.e., histograms). The algorithm is based on two steps: (i) construction of a graph connecting patches of pixels with similar temporal responses, and (ii) estimation of the DI values for pixels belonging to homogeneous spatial classes. The first step is achieved by building a graph representation of the 3D data, while giving a special attention to the computational complexity of the algorithm. The second step is achieved using a Fisher scoring gradient descent algorithm while accounting for the data statistics and the Laplacian regularization term. Results on laboratory data show the benefit of the proposed strategy that improves the quality of the estimated DI images.

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Halimi A, Connolly P, Ren X, Altmann Y, Gyongy I, Henderson RK et al. Restoration of Depth and Intensity Images using a Graph Laplacian Regularization. In 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE. 2018. 8313136 https://doi.org/10.1109/CAMSAP.2017.8313136