Ghost Imaging by a Proportional Parameter to Filter Bucket Data

Abstract

Most ghost imaging reconstruction algorithms require a large measurement time to retrieve the object information clearly. But not all groups of data play a positive role in reconstructing the object image. Abandoning some redundant data can not only enhance the quality of reconstruction images but also speed up the computation process. Here, we propose a method to screen the data using two threshold values set by a proportional parameter during the sampling process. Experimental results show that the reserved data after screening can be used in several reconstruction algorithms, and the reconstruction quality is enhanced or at least remains at the same level. Meanwhile, the computing time costs are greatly reduced, and so is the data storage.