Optimization Of Compression Sensing Imaging Algorithm In Correlation Imaging

Correlation imaging technology is to use the second-order correlation characteristics of light field to obtain the spatial state information of the object,because of its detection and object separation characteristics,also known as ghost imaging.Compared with traditional optical imaging methods such as microscope and camera,which use the first-order correlation characteristics of light field to obtain object image information,correlation imaging has better disturbance resistance and has the advantage of imaging in a more complex environment,so it is widely used in biomedical,radar,remote sensing detection imaging and other fields.But due to the need to use multiple readings for the reconstruction of the object information,caused the shortcoming of low efficiency,long reconstructed image and the compression technology in the field of correlation imaging studies have shown that perception,can under the condition of is far lower than the Nyquist sampling number is good enough to restore the image of the object under test,scholars also hope that through the compression perception technology improve the imaging speed,Improve imaging quality and solve the burden of data acquisition and transmission caused by the need for a large number of samples.In associated imaging,the object reconstruction using compressed sensing algorithm needs to consider the sparsity of the object to be measured and the setting of measurement matrix.How to make good use of the sparse characteristics of the object and select the appropriate measurement matrix affect and limit the reconstruction results of the compressed sensing method to a certain extent.Based on the research of correlation imaging and compressed sensing,this paper proposes a method to preprocess the reference light field by using the orthotopic method.Combined with the compressed sensing method,this method aims to further reduce the sampling times and improve the quality of reconstructed images.This paper mainly completes the comparative study on reconstruction results of correlation imaging by different compressive sensing methods.Aiming at the proposed preprocessing compressed sensing method,different noise environments were simulated under simulation conditions to verify the effectiveness of the preprocessing method in improving the reconstruction quality of the compressed sensing algorithm.Complete the experimental scheme of transmission equal-arm associated imaging,set experimental device parameters and collect experimental data;The performance of different parameter Settings on reconstructed images is discussed on the basis of simulation,and the proposed method is verified and applied on experimental data.The simulation and experimental results show that the compressed sensing method using preprocessed light field can effectively improve the reconstructed image quality under different noise environments and lower sampling times.