Multi-resolution reconstruction algorithm for compressive single pixel spectral imaging. Garcia, H., Correa, C. V., Villarreal, O., Pinilla, S., & Arguello, H. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 468-472, Aug, 2017.
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Paper doi abstract bibtex
Paper doi abstract bibtex Spectral imaging is useful in a wide range of applications for non-invasive detection and classification. However, the massive amount of involved data increases its processing and storing costs. In contrast, compressive spectral imaging (CSI) establishes that the three-dimensional data cube can be recovered from a small set of projections, that are generally captured in 2-dimensional detectors. Furthermore, the single-pixel camera (SPC) has been also employed for spectral imaging. Specifically, the SPC captures the spatial and spectral information in a single measurement. CSI reconstructions are traditionally obtained by solving a minimization problem using iterative algorithms. However, the computational load of these algorithms is high due to the dimensionality of the involved sensing matrices. In this paper, a multi-resolution (MR) reconstruction model is proposed such that the complexity of the inverse problem is reduced. In particular, this model uses the spectral correlation to group pixels with similar spectral characteristics. Simulation results show that the MR model improves the reconstruction PSNR in up to 9dB with respect to the traditional methods. In addition, the proposed approach is 79% faster, using only 25% of the measurements.
@InProceedings{8081251,
author = {H. Garcia and C. V. Correa and O. Villarreal and S. Pinilla and H. Arguello},
booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
title = {Multi-resolution reconstruction algorithm for compressive single pixel spectral imaging},
year = {2017},
pages = {468-472},
abstract = {Spectral imaging is useful in a wide range of applications for non-invasive detection and classification. However, the massive amount of involved data increases its processing and storing costs. In contrast, compressive spectral imaging (CSI) establishes that the three-dimensional data cube can be recovered from a small set of projections, that are generally captured in 2-dimensional detectors. Furthermore, the single-pixel camera (SPC) has been also employed for spectral imaging. Specifically, the SPC captures the spatial and spectral information in a single measurement. CSI reconstructions are traditionally obtained by solving a minimization problem using iterative algorithms. However, the computational load of these algorithms is high due to the dimensionality of the involved sensing matrices. In this paper, a multi-resolution (MR) reconstruction model is proposed such that the complexity of the inverse problem is reduced. In particular, this model uses the spectral correlation to group pixels with similar spectral characteristics. Simulation results show that the MR model improves the reconstruction PSNR in up to 9dB with respect to the traditional methods. In addition, the proposed approach is 79% faster, using only 25% of the measurements.},
keywords = {cameras;correlation methods;image reconstruction;image resolution;inverse problems;iterative methods;matrix algebra;storing costs;compressive spectral imaging;three-dimensional data cube;2-dimensional detectors;single-pixel camera;SPC;spatial information;spectral information;CSI reconstructions;iterative algorithms;multiresolution reconstruction model;spectral correlation;group pixels;reconstruction PSNR;multiresolution reconstruction algorithm;compressive single pixel spectral imaging;sensing matrices;CSI;minimization problem;inverse problem;noise figure 9.0 dB;Image reconstruction;Imaging;Signal processing algorithms;Image resolution;Detectors;Apertures},
doi = {10.23919/EUSIPCO.2017.8081251},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347321.pdf},
}Downloads: 0
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