## 3 Experimental Scheme and Analysis of Results
### Experimental Results and Cause Analysis
Similar to the results observed by the naked eye, the untrained neural network method shows the best performance at all sampling rates, especially at low sampling rates, and is also robust to noise, which helps to reduce the number of samples required for actual imaging in order to reduce the memory footprint, as well as possible damage to the optics. Comparison of PSNR values of images reconstructed by different reconstruction algorithms with respect to sampling rate is shown in Figure 6. It is clear that the PSNR value of each reconstruction algorithm increases with the increase in the sampling rate, and the DL reconstructed image has the highest PSNR value at the same sampling rate. We calculate the PSNR value of the original intensity image of the camera to be 8.51 dB, and when the sampling rate is 6.25%, the PSNR values of CGI and BP are smaller than those of the  
Because the ToF camera will be affected by the scattering medium when imaging through the scattering medium, resulting in a degradation of image quality, it cannot work properly under conditions with scattering medium such as underwater and haze. According to the SPI, it has the advantages of strong anti-interference ability, which can well resist the influence of atmospheric turbulence and scattering medium on the imaging quality. When the ToF camera fails in the environment with scattering medium, in order to improve the quality of the ToF camera in imaging through scattering medium, our proposed scheme of combining the ToF camera and SPI is applied to the camera imaging through scattering medium. In order to test whether the proposed scheme can image an object through a scattering medium, we inserted a 0.5 mm opaque plastic sheet as a scattering medium in the optical path between the ToF camera and the TRP in Figure 4, so that the light beam illuminated on it is scattered, and other experimental conditions remain unchanged. The data collected by the ToF camera can be divided into the information about the object affected by the scattering medium and the information about the object not affected by the scattering medium. The imaging of the target object information in the ToF camera can be expressed as  
\[S(x)=\alpha S(x)+S^{D}(x), \tag{18}\]  
Here, \(\alpha S(x)\) is the information distribution of the reflected light of the object, \(S^{D}(x)\) is the information distribution of the transmitted light of the object, \(\alpha(0<\alpha<1)\) is the transmittance ratio of the scattering medium, and other experimental conditions remain unchanged. Figure 7a is the intensity image of the target object obtained directly by using the ToF camera through the scattering medium, and we cannot see the outline information of the object at all. Figure 7b-e shows the images recovered by using the four algorithms of CGI, BP, TVAL3, and DL with intensity information through the scattering medium at different sampling rates. When the sampling rate is 6.25%, the CGI, BP, and TVAL3 algorithms that reconstruct the image of the object can only see some fuzzy streaky blocks, but compared with the intensity image directly obtained by using the ToF camera through the scattering medium, the three algorithms of CGI, BP, and TVAL3 improve the quality of the image through the scattering medium, whereas the untrained DL-based method is able to see the contours of the object and can recover better target images than the other three methods. When the sampling rate is 18.75, CGI, BP, and TVAL3 are able to see a clearer image of the object to be measured, but it is clear that the quality of the images obtained using CGI and TVAL3 is higher than that of BP, and the untrained DL is more effective than the other three algorithms. When the sampling rate is 25%, the background noise of the  
Figure 6: Plots of the PSNRs of the reconstructed intensity images versus the SRs by different algorithms. The black, red, blue, and green lines denote the PSNRs by CGI, BP, TVAL3, and DL.