# Binary Ghost Imaging Based on the Fuzzy Integral Method
## 3 Experiment Results and Comparative Analysis
As Figure 5c,d show, The SSIMs of the reconstructed results by the local adaptive binarization methods (PPBGI and FIBGI) are higher than those by other methods (OBGI, MBGI, and DGI). Moreover, compared with the PPBGI, the proposed method is better. When the number of measurements is small, the SSIM of reconstructed results with the proposed method is relatively low, but still higher than other algorithms. With the increase in measurement numbers, the SSIM of the proposed algorithm improves rapidly and remains optimal. In addition, a grayscale image is selected as the experimental target to verify the superior reconstruction ability of the proposed method. A grayscale house image is selected as the target. The resolution of the selected grayscale image is 128 \(\times\) 128. The number of measurements is 10,000. The reconstructed results with different methods are shown in Figure 6. The images in the first row of Figure 6 are the selected target and reconstructed results with various methods. Similar to the case of binary target, the result reconstructed by the proposed method has the best overall visual effect. The resolution of the reconstructed result with the FIBGI is better than those with other methods. To make the contrast more obvious, the reconstructed result in the red rectangular area is enlarged and shown in the second row of Figure 6. It can be seen from the second row of Figure 6 that much detail information is lost in the reconstructed results with DGI, MBGI, and OBGI. The window frame, the edge of the wall and the water pipeline look blurry. Although some outline information of these regions can be seen in the results reconstructed by the PPBGI method, the resolution is still not high. Especially, there is a slight checkerboard artifact in the reconstruction result of the PPBGI. In comparison, the result reconstructed by the proposed method has the highest image quality. The detail of the reconstructed image is relatively clear and obvious compared with the global binarization methods, such as MBGI and OBGI, and there is no checkerboard artifact compared with the PPBGI method which also adopts local adaptive binarization method. The PSNR and SSIM of the reconstructed results of grayscale target are also calculated and shown in Figure 7. As Figure 7 shows, similar to the binary target, the PSNRs and SSIMs of all the methods improve with the increase in the number of measurements. The PSNRs of the local binarization methods are lower than those of the global binarization methods at different measurements, as there is more noise in the reconstruction results with the local binarization methods. The reconstructed result of the proposed method performs well in SSIM. When the number of measurements is low, the SSIMs of the reconstruction results of different methods are  
Figure 5: The quantitative comparison of reconstruction results by different methods. (**a**) The PSNRs of reconstructed Chinese character with different methods changing with the number of measurements. (**b**) The PSNRs of reconstructed image panda with different methods changing with the number of measurements. (**c**) The SSIMs of reconstructed Chinese character with different methods changing with the number of measurements. (**d**) The SSIMs of reconstructed image panda with different methods changing with the number of measurements. relatively low, and there is little difference between each other. With the increase in the number of measurements, the SSIMs of the reconstructed results using various methods improve, and the SSIM of the proposed method has the fastest improving rate. When the number of measurements reaches 10,000, the SSIM of the proposed method has a higher value than the ones of other methods. It can be concluded from the quantitative comparisons that the reconstructed results with global binarization methods and DGI have relatively low noise level, and therefore the PSNRs are high. Although the results reconstructed by local binarization methods have higher noise level, the results are more similar to the target.