## 4 Experiment
### Results
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & **Model 1** & **Model 2** & **Model 3** & **Model 4** \\ \hline \multirow{3}{*}{SVD} & PSNR & 33.8807 & 33.393 & 33.3803 & 33.7289 \\ \cline{2-6}  & SSIM & 0.8446 & 0.8439 & 0.8104 & 0.3016 \\ \cline{2-6}  & LPIPS & 0.2557 & 0.275 & 0.2852 & 0.3179 \\ \hline \multirow{3}{*}{TV} & PSNR & 33.3527 & 33.6744 & 33.5414 & 33.5869 \\ \cline{2-6}  & SSIM & 0.8623 & 0.8711 & 0.8242 & 0.8228 \\ \cline{2-6}  & LPIPS & 0.2449 & 0.2285 & 0.2811 & 0.2905 \\ \hline \multirow{3}{*}{Accelerated TV} & PSNR & 34.0245 & 33.5931 & 33.5596 & 33.5742 \\ \cline{2-6}  & SSIM & 0.8612 & 0.8526 & 0.8269 & 0.8367 \\ \cline{2-6}  & LPIPS & 0.2292 & 0.254 & 0.2737 & 0.2499 \\ \hline \multirow{3}{*}{TVAL3} & PSNR & 34.468 & 33.388 & 33.5291 & 34.0917 \\ \cline{2-6}  & SSIM & 0.8773 & 0.8824 & 0.8482 & 0.8421 \\ \cline{2-6}  & LPIPS & 0.1831 & 0.2085 & 0.2782 & 0.2687 \\ \hline \multirow{3}{*}{Ours} & PSNR & 34.5091 & 33.7087 & 33.6526 & 34.136 \\ \cline{2-6}  & SSIM & 0.8858 & 0.8894 & 0.8513 & 0.8516 \\ \cline{1-1} \cline{2-6}  & LPIPS & 0.1657 & 0.2143 & 0.247 & 0.2588 \\ \hline \end{tabular}
\end{table} 
Table 1: Numerical results reconstructed by different methods for different models. Based on the data provided in Table 2, it is clear that our algorithm surpassed the other methods when considering the evaluation indicators of the SSIM and LPIPS. It secured the second position in terms of the PSNR. Although all three methods produced reconstructed images with artifacts, the ones generated by SOGTV demonstrated the most-effective reduction of these artifacts. It is important to acknowledge that actual measurements may be susceptible to errors in measurement, errors in the model, and interference from environmental noise. As a result, the signal-to-noise ratio of the measured voltage was reduced, leading to a lower quality of the actual reconstructed image compared to the simulated calculation. This further strengthened the conclusion that SOGTV provides superior imaging quality. Figure 4: An EIT experimental setup. (**a**) Measured graph of constant current source output current. (**b**) Resistor loop circuit diagram. Figure 5: (**a**) Physical model of a circular sink. (**b**) Homogenized sodium chloride solution. (**c**) With acrylic column. Figure 6: Visualization of a circular sink with acrylic cylinders using a physical model. At last, to validate the convergence of the algorithm proposed, numerical experiments were performed. We used the model in Figure 6 and reconstructed the EIT image with three algorithms. As shown in Figure 7, the variation in \(CF\) as the number of iterations progressed is demonstrated. As the number of iterations increased, \(CF\) approached zero. The \(CF\) is calculated as follows [50]. \[\text{Out}\,_{k}=\frac{1}{2}\
orm{Ag-b_{\text{meas}}}_{2}^{2}+TV_{ \beta}(g)\] \[CF=\frac{\abs{Out_{k}-Out_{k-1}}}{Out_{k-1}}\]  
From Figure 7, the SOGTV algorithm has a faster convergence rate compared with Accelerated TV and TVAL3. The imaging results demonstrated that our algorithm effectively identified the perturbation position of the acrylic cylinder. Although some artifacts were still present, a comparison of the relevant indicators confirmed that our imaging quality surpassed that of the other algorithms. This lays a solid foundation for future imaging studies. The research and enhancement of algorithms contribute to new ideas in this field. EIT finds application in various medical imaging fields including lung imaging, breast imaging, and brain monitoring. It serves as a valuable complement to other imaging technologies like X-ray, CT, and MRI due to its advantages in real-time monitoring, non-invasiveness, and cost-effectiveness.