The Background Light Noise Resistance Implementation For Ghost Imaging Based On FADOF

Different from the traditional imaging methods,ghost imaging is an indirect imaging method.Due to the advantages of single point detector imaging,low-light imaging,anti-scattering medium interference imaging and hyperspectral imaging,ghost imaging has great potential in some fields.The main challenge in ghost imaging is how to obtain high quality images with a small number of measurements while achieving a balance between imaging time and computational resources.With the efforts of many researchers,the problem is gradually being solved and ghost imaging is trying to move towards applications.However,there is still a problem that cannot be ignored in the practical application of ghost imaging,and this problem is the interference of background light noise.The varying background light can interfere with the bucket detector measurement,making the bucket-side signal error,which in turn affects the quality of the reconstruction image of the ghost imaging.This problem makes it difficult for ghost imaging to work stably for a long time in outdoor environments.To address this problem,this paper propose that use a Faraday anomalous dispersion optical filter(FADOF)to eliminate the influence of background light noise in ghost imaging.In this paper,we first theoretically analyze and simulate the influence of background noise on ghost imaging,and discuss the noise resistance principle and performance of ghost imaging based on FADOF.Then we specifically implement the system and describe the key details such as FADOF state analysis debugging and ghost imaging light source frequency stabilization.At the same time,we tested the noise resistance performance of the system under laboratory conditions.The experimental results show that the method has a simple structure,outstanding performance,good algorithm compatibility,and maintains the normal working of ghost imaging in an environment where the background light noise is thousands of times that of the signal.Finally,we discuss how to further optimize the system from an experimental point of view.