# 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.  
imaging processing; ghost imaging; photon statistics; threshold selection +
Footnote †: journal: Medical Engineering and Technology  
0000-0001-6000-0001-6000][PERSON] 00000-0001-6000-0001-6000][PERSON] 0000-0001-6000-0001-60000][PERSON] 0000-0001-6000-0001-60000][PERSON] 0000-  
some researchers focus on setting threshold values to screen the sampling data [24; 25; 26]. For example, the method using positive and negative correlations introduces the mean value of the speckle field information obtained by the reference arm as a threshold [25], in which all the sampled data are divided into two detailed sets, so each pair of speckle fields is given two labels. Double-threshold time-correspondence imaging (DTTCI) is a method of setting threshold values [26], but it needs an additional bucket detector in the reference arm of the conventional GI experimental system. In addition, computing ghost imaging (CGI) utilizes a spatial light modulator to save the construction of the reference light path, which greatly simplifies the GI experimental system and further facilitates the practical application of GI [27; 28]. Recently, deep learning, as a research hotspot, has been applied in many fields, and some researchers apply deep learning in GI [29; 30], but it requires vast amounts of data to train its network.  
Experiments prove that DTTCI has a good imaging effect in NGI and DGI, but not for other reconstruction algorithms. The reason is that their imaging formulas contain the ratio of the measured data of the object arm bucket detector to the reference arm bucket detector and its average value. Therefore, we propose a novel and simple method by setting double threshold values according to a proportional parameter to screen the data in the sampling process. First, it counts the total light intensity data of the object arm bucket detector with only a few hundred measurements and sets two threshold values (a large one and a small one) in advance. Then, during the sampling process, it only records the total light intensity, which is higher than the larger threshold or lower than the smaller one. Their corresponding light field distribution of the reference arm at the same time and the data that does not meet the threshold condition will be abandoned. The experimental results show that this method can greatly reduce the time cost during the reconstruction procedure and keep the same, or sometimes even higher, levels of reconstruction quality using less screened data. Moreover, this method can also be used in other algorithms, such as PGI, SMGI.