Thermal light ghost imaging based on morphology

•This is a manuscript about thermal ghost imaging. It is novel that the morphological technology is introduced into thermal ghost imaging to decrease undersampling noise and enhance the SNR of ghost imaging.•Undersampling noise is generated because of limited sampling, which submerges the object information and decreases the SNR of ghost-imaging system.•According to an analysis of this manuscript, we find that the second-order correlation function is the most important factor that impacts the characteristics of undersampling noise.•Therefore, we can design a denoise module based on the second-order correlation function, then undersampling noise could be eliminated through the use of a morphological filter, including erosion and dilation.•Experimental results show the effectiveness of this method.

The quality of thermal light ghost imaging could be degraded by undersampling noise. This kind of noise is generated because of finite sampling, which could reduce the signal-to-noise ratio (SNR) of ghost imaging and submerge object information. In order to reduce the undersampling noise, we propose a thermal light ghost imaging scheme based on the morphology (GIM). In this scheme, the average size of the undersampling noise can be obtained by computing the second-order correlation function of the ghost imaging system. According to the average size of the undersampling noise, the corresponding structure element can be designed and used in the morphological filter; then, the GIM reconstructed image can be obtained. The experiment results show that the peak signal-to-noise ratio of the GIM reconstructed image can increased by 80% than that of conventional ghost imaging for the same number of measurements.