Emission Ghost Imaging: reconstruction with data augmentation

Published

February 1, 2024

Author(s)

Kevin J. Coakley, Heather H. Chen-Mayer, Bruce D. Ravel, Daniel Josell, Nikolai Klimov, Sarah Robinson, Daniel S. Hussey

Abstract

Ghost Imaging enables 2D reconstruction of an object even though particles transmitted or emitted by the object of interest are detected with a single pixel detector without spatial resolution. This is possible because the incident beam is spatially modulated with an attenuating mask with many distinct orientations where each orientation yields a distinct spatial pattern in the attenuated beam. In many cases, ghost imaging reconstructions can be dramatically improved by factoring the measurement matrix which consists of measured attenuated incident radiation for each of many orientations of the mask at each pixel to be reconstructed as the product of an orthonormal matrix Q and an upper triangular matrix R provided that the number of orientations of the mask N is greater than or equal to the number of pixels P reconstructed. For the N < P case, we present a data augmentation method that enables QR factorization of the measurement matrix. To suppress noise in the reconstruction, we determine the Moore-Penrose pseudoinverse of the measurement matrix with a truncated singular value decomposition approach. Since the resulting reconstruction is still noisy, we denoise it with the Adaptive Weights Smoothing method. In simulation experiments, our method outperforms a modification of an existing alternative orthogonalization method where rows of the measurement matrix are orthogonalized by the Gram-Schmidt method. We apply our ghost imaging methods to experimental X-ray fluorescence data acquired at Brookhaven National Laboratory.

Citation

Physical Review A

Volume

109

Pub Type

Journals

Download Paper

https://doi.org/10.1103/PhysRevA.109.023501

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Keywords

Adaptive Weights Smoothing, Data augmentation, Denoising, Ghost imaging, Monte Carlo, $QR$ factorization, Synchrotron X rays, Truncated singular value decomposition, X-ray fluorescence.

Citation

Coakley, K. , Chen-Mayer, H. , Ravel, B. , Josell, D. , Klimov, N. , Robinson, S. and Hussey, D. (2024), Emission Ghost Imaging: reconstruction with data augmentation, Physical Review A, [online], https://doi.org/10.1103/PhysRevA.109.023501, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956476 (Accessed October 17, 2025)