An Effective Computational Ghost Imaging Based on Noise Estimation and Elimination

To improve the quality of ghost image, we propose an efficient computational ghost imaging method in this article. The primary idea is to estimate the noise value of the ghost image by analyzing and eliminating the source of the noise. The innovativeness of this work lies in analyzing a new means of noise by dissecting the qualitative relationship between transmittance in different objects and speckle patterns. While using a scale factor to describe the change of transmittance at different points of the object. The simulation and experimental results prove the effectiveness and feasibility of the proposed method through two parallel experiments. Compared to other methods, the peak signal-to-noise ratio and contrasts both have significantly increased.