Gerchberg-Saxton-like ghost imaging.

Abstract

Correlation is widely used to reconstruct the object image in ghost imaging (GI). But it only offers a linear proportion of the signal-to-noise ratios (SNR) to the number of measurements. We develop a Gerchberg-Saxton-like technique for GI image reconstruction in this manuscript. The proposed technique takes the advantage of the integral property of the Fourier transform, and treat the captured data as constraints for image reconstruction. We numerically and experimentally demonstrate the technique, and observe a nonlinear growth of the SNR value with respect to the number of measurements in the simulation. The proposed technique provides a different perspective of image reconstruction of GI, and will be beneficial to further explore its potential.