The Optimization Of Illumination Field In Quantum Imaging

Quantum imaging,also called as ghost imaging,is a new imaging method that utilizes the second-order and higher-order correlation properties of optical field.Different from the traditional first-order imaging,quantum imaging can separate the image from the object,and then recover the obj ect through correlation calculation.Quantum imaging has many advantages,such as in vitro imaging,good anti-noise performance,and even super resolution.Quantum imaging has a wide range of application scenarios in practical life so that it has received great attention and research from scholars.However,Quantum imaging also has its drawbacks.Quantum imaging requires the detector to carry out multiple exposure sampling for the target object to obtain enough sampling data.So,the sampling time is long and the data volume is large,which has many limitations on data collection,data processing,hardware facilities and quantum imaging recovery algorithm.Therefore,it is particularly urgent and critical to find an effective method that can reduce the amount of sampled data without reducing the quality of restored image.In this case,compressive sensing method emerged.Compressive sensing method is a new theoretical method of signal acquisition,which points out the sampling frequency can be much lower than Nyquist frequency to restore all the information of the target object as long as the collected signals meet some specific conditions.In one hand,the signal can be collected by compression by using this way.On the other hand,the study on the periodicity of the reference signal in the reference optical path can also achieve the goal of reducing the amount of sampled data.The main research contents as follows:(1)Introduce the theoretical background of quantum imaging and compressive sensing method.Analyze the advantages and disadvantages of quantum imaging,and combines the compressed sensing algorithm with quantum imaging to form compressed sensing quantum imaging.Compressed sensing theory can show that only a small amount of sampled data can restore the same quality image.In this paper,the comparison experiment shows that the compressed sensing algorithm can get better image quality when using the same amount of data,which shows that the compressed sensing algorithm can recover the same image quality and improve the correlation imaging efficiency with less sampled data.(2)Introduce the experimental principle of quantum imaging,analyze the signal light path and the reference optical path to find a way of reducing the amount of sampled data.Quantum imaging experiments show that the signal in the reference optical path is based on the periodic rotation of frosted glass,so there may be some periodicity.If it exists,it can use the periodic principle to greatly reduce the huge amount of sampled data,optimize the correlation imaging experimental architecture,and improve the efficiency of the associated imaging experiment.The research shows it was found that the recovery image experiment by periodically replacing the original data was not successful due to the positioning accuracy and environmental interference.(3)Resolution has always been one of the key indicators of imaging systems.Previously,scholar Candes suggested that the light field and convex optimization methods will have an impact on the resolution.This paper mainly studies the effects of different light fields and different convex optimization methods on resolution.The research shows the resolution of different light fields is different.With different optimization methods,the resolution will also change.Even in extremely sparse cases,the resolution can reach infinity which just verifies the theoretical analysis of the scholar Candes.At the same time,it is found that the fluctuation pretreatment has an improvement effect on the restoration of image quality.The experimental results show that the compressed sensing algorithm after the fluctuation preprocessing is better than the traditional compressed sensing algorithm.