## 5 Experimental Results and Analyses
### Testing and Comparing the Denoising Performance of Different Algorithms
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{_Level_} & \multirow{2}{*}{**Image**} & \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**The Output Seismic Signal**} \\ \cline{4-5}  & & & **PSNR (dB)** & **SSIM** & **Time (s)** \\ \hline \multirow{8}{*}{Lena} & ATV & 25.85 & 0.796 & 5 \\  & ITV & 25.8 & 0.8009 & **3.11** \\  & TGV & 26.2 & 0.8041 & 10.27 \\  & OGS-L1 & 26.22 & 0.8151 & 11.25 \\  & OGS-Lp & 27.64 & 0.8683 & 18.88 \\  & OGS-Lp-FAST & **27.67** & **0.8675** & 9.81 \\  & ATV & 28.5 & 0.8433 & 4.94 \\  & ITV & 28.69 & 0.8356 & **2.31** \\  & TGV & 29.21 & 0.8284 & 9.48 \\  & OGS-L1 & 29.31 & 0.8566 & 11.95 \\  & OGS-Lp & 32.84 & 0.9182 & 13.77 \\  & OGS-Lp-FAST & **32.92** & **0.9189** & 12.8 \\  & ATV & 32.4 & 0.9328 & 4.86 \\  & ITV & 32.54 & 0.9357 & **3.86** \\  & TGV & 32.8 & 0.9345 & 24.81 \\  & OGS-L1 & 32.62 & 0.9310 & 16.36 \\  & OGS-Lp & **33.25** & **0.9510** & 20.66 \\
40\% & OGS-Lp-FAST & 33.24 & 0.9509 & 17.61 \\  & ATV & 27.19 & 0.8348 & 4.94 \\  & ITV & 27.33 & 0.8447 & **3.09** \\ Girl & TGV & 27.86 & 0.8135 & 12.11 \\  & OGS-L1 & 27.88 & 0.8526 & 11.78 \\  & OGS-Lp- & **28.87** & **0.8861** & 18.05 \\  & OGS-Lp-FAST & 28.86 & 0.8847 & 12.69 \\  & ATV & 27.51 & 0.8307 & 3.97 \\  & ITV & 28.1 & 0.8424 & **3.19** \\ Milk Drop & TGV & 28.09 & 0.8521 & 12.97 \\  & OGS-L1 & 29.34 & 0.8569 & 10.27 \\  & OGS-Lp & **30.56** & **0.9398** & 16.94 \\  & OGS-Lp-FAST & 30.46 & 0.8933 & 13 \\  & ATV & 26.97 & 0.8338 & 4.67 \\  & ITV & 27.13 & 0.8366 & **3.63** \\ Woman & TGV & 27.34 & 0.7708 & 9.58 \\  & OGS-L1 & 27.7 & 0.8483 & 13.13 \\  & OGS-Lp & 28.27 & 0.8722 & 18.45 \\  & OGS-Lp-FAST & **28.29** & **0.8716** & 13.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Numerical comparison of our proposed method and other models (images are corrupted by impulse noise of 40%). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{_Level_} & \multirow{2}{*}{**Image**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**The Output Seismic Signal**} \\ \cline{3-5}  & & & **PSNR (dB)** & **SSIM** & **Time (s)** \\ \hline \multirow{8}{*}{Girl} & ATV & 28.17 & 0.8586 & 5.17 \\  & ITV & 28.53 & 0.8737 & **3.31** \\  & TGV & 28.82 & 0.8422 & 8.84 \\  & OGS-L1 & 29.11 & 0.8818 & 8.88 \\  & OGS-Lp & **30.42** & **0.9155** & 15.64 \\  & OGS-Lp-FAST & 30.41 & 0.9148 & 12.84 \\  & ATV  
To verify our proposed method further, we compared the image details restored by different algorithms. Figure 6 shows four images (\"Woman\", \"Pepper\", \"Girl\", and \"House\") with impulse noise from 20% to 50%. The enlarged details of the images restored by ATV, ITV, TGV, OGS-L1, and OGS-Lp-FAST algorithms are displayed for comparison. In terms of the visual effect of the restored images, ATV denoising produces apparent blocking artifacts in the images. In the \"Pepper\" image ATV recovers, we can easily find two heavy noise pixels. The ITV method also shows significant staircase effect. In the \"House\" image it restores, the edge of the image is not well preserved when the noise pollution is high. For the denoising results of TGV, the blocking artifacts in the images are sufficiently suppressed, but local heavy noise spots are still observable. Finally, by comparing the four images, we can easily see the visual improvement in the images by using our method. Even at high noise pollution, our method protects the edge information of the image very well, and at the same time, avoids the staircase effect.