# Ghost Imaging by a Proportional Parameter to Filter Bucket Data
## 3 Experimental Results  
All the experiments were carried out on the setup as shown in Figure 1, in which the distance L1 (L1 = L2) is 200 mm, the laser wavelength \(\lambda\) = 532 nm, the focal length f of the lens was 150 mm, CCD1 (Stingray F-504B, AVT, Stadtroda, Germany) was identical to CCD2, CCD1 was used to gather the sum of light field distribution modulated by the Object and CCD2 was used to record the light field distribution of reference beam at the surface of the object. First of all, to test how effective our method was for the conventional GI algorithm expressed by Equations (1) and (4), we respectively screened 2000, 3000, and 4000 groups of data according to the threshold values determined by the corresponding proportional parameter C, from the total 5000 groups of sampled data to reconstruct the targets \"GI\" and \"ZHONG\", and the reconstruction results are shown in Figure 3. Using Peak Signal-to-Noise Ratio (PSNR) as the quantitative index to evaluate the quality of the reconstructed image, the results are shown in Figure 4, in which the upper and lower dotted lines represent the PSNR values of reconstruction results when the number of data groups was 5000. From Figure 3, it is obvious that the visual effects using 2000, 3000, and 4000 groups of data were almost the same as all the groups of data (5000 groups). From Figure 4, we can see that when the \(C\) was no less than 0.6, the PSNR of the reconstructed results remained the same quality or slightly improved if some data were abandoned. It can also be seen  
Figure 4: Peak Signal-to-Noise Ratio (PSNR) curves vs. C of the reconstruction result of “GI” and “ZHONG”. The upper and lower dotted lines represent the PSNR values of reconstruction results when the number of data groups was 5000. Figure 3: Experimental results for the “GI” and “ZHONG” obtained by the GI with 2000, 3000, 4000, and 5000 groups of data. (**a,f**) original image with 80 \(\times\) 80 pixels; (**b,g**) the reconstructions with 2000 groups of data(**c,h**) the reconstructions with 3000 groups of data; (**d,i**) the reconstructions with 4000 groups of data; (**e,j**) the reconstructions with 5000 groups of data. from Figure 4 that when \(C=0.6\) to \(0.7\), the reconstruction quality was better than that of GI using all sampled data. Multiple experimental results showed that \(C=0.65\) was applicable to all binary images tested. In other words, the parameter \(C\) between \(0.6\) and \(0.7\) was a general optimal solution. Therefore, when reconstructing a target, we could choose the minimum and maximum of the total light intensity in the rejection data when \(C=0.65\) as two thresholds. Moreover, the reason why the PSNR values go slightly down with the increase in the \(C\) value (The amount of data used for reconstruction was also increasing) was that the effect of the deleted data groups might bring more noise so as to affect the improvement in reconstruction quality, which also proved that if the data were not filtered, there would be a considerable amount of useless data in the sampled data and they would have a side effect on the reconstruction quality. The experiment showed that our method can keep the quality of reconstruction basically unchanged when it deletes a certain number of the sampled data. Meanwhile, the computing time is greatly reduced since the screening process does not take much time. For example, when \(C=0.6\), using the filtered data could save about 15 s computing time (the computer environment: CPU i5-8300H, memory 8G, Lenovo, Beijing, China) under the premise of ensuring the reconstruction quality. From the above, we demonstrated the effectiveness of our method on the GI algorithm. According to the experiments above, the optimum range of \(C\) was \(0.6\)\(\sim\)\(0.7\), so in the subsequent experiments (procedure of sampling and storing data), we selected the maximum and minimum values of Bn corresponding with \(C=0.65\) as the two thresholds and carried out the sampling procedure combined with the filtering process.