Object reconstitution using pseudo-inverse for ghost imaging

Abstract

We propose a novel method for object reconstruction of ghost imaging based on Pseudo-Inverse, where the original objects are reconstructed by computing the pseudo-inverse of the matrix constituted by the row vectors of each speckle field. We conduct reconstructions for binary images and gray-scale images. With equal number of measurements, our method presents a satisfying performance on enhancing Peak Signal to Noise Ratio (PSNR) and reducing computing time. Being compared with the other existing methods, its PSNR distinctly exceeds that of the traditional Ghost Imaging (GI) and Differential Ghost Imaging (DGI). In comparison with the Compressive-sensing Ghost Imaging (CGI), the computing time is substantially shortened, and in regard to PSNR our method exceeds CGI on grayscale images and performs as well as CGI visually on binary images. The influence of both the detection noise and the accuracy of measurement matrix on PSNR are also presented.

What are the advantages of ghost imaging? Multiplexing for x-ray and electron imaging

“…While the ordinary least squares solutions are well known, for completeness in Appendix A we compactly prove (3) and (4). We are motivated to do so by the fact that in the field of ghost imaging it is still common to use sub-optimal estimators, even though least squares has been employed previously [16][17][18]. The properties and performance of the traditionally used ghost imaging estimator is discussed in Appendix B and shown numerically in Fig.…”

Speckle-Shifting Ghost Imaging

“…(a) Various computational methods focused on improving the imaging quality of GI, e.g. (1) Differential ghost imaging [15], (2) compressive ghost imaging [16], (3) pseudoinverse ghost imaging [17,18], (4) iterative ghost imaging [19,20], sinusoidal ghost imaging [21] and adaptive computational ghost imaging [22]. However, there exists limitation for heavily absorbing objects or they are computationally complex and expensive, and thus difficult for practical applications.…”

Improving signal-to-noise ratio performance of compressive imaging based on spatial correlation

“…Several methods have been proposed to solve this problem, (1) sampling with an extra measurement to estimate the intensity of noise, (2) ghost imaging (GI), (3) computational constraints in reconstruction, etc. However, there exists limitation for volatile noise and expensive computation, or they are limited by the speckle transverse size [13][14][15][16][17]. In this paper, we introduce CISC to improve signal-to-noise ratio (SNR) performance of CI without expensive computation and any additional hardware.…”