## 5 Experimental Results and Analyses
### Testing and Comparing the Denoising Performance of Different Algorithms
For example, at a 20% noise level, as shown in Table 2, the time value of the \"Woman\" image (8.69 s) given by the OGS-Lp-FAST model is 7.53 s less than that given by the OGS-L1 model (16.22 s). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{_Level_} & \multirow{2}{*}{**Image**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**The Output Seismic Signal**} \\ \cline{4-6}  & & & **PSNR (dB)** & **SSIM** & **Time (s)** \\ \hline \multirow{4}{*}{20\%} & & ATV & 28.71 & 0.8854 & 4.81 \\  & & ITV & 28.83 & 0.8936 & **2.45** \\ \cline{1-1}  & & TGV & 28.63 & 0.8966 & 9.59 \\ \cline{1-1}  & & OGS-L1 & 29.79 & 0.9115 & 16.34 \\ \cline{1-1}  & & OGS-Lp & 31.47 & 0.9474 & 13.94 \\ \cline{1-1}  & & OGS-Lp-FAST & **31.55** & **0.9482** & 8.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Numerical comparison of our proposed method and other models (images are corrupted by impulse noise of 20%). ATV: anisotropic total variation; ITV: isotropic total variation; TGV: total generalized variation; OGS-L1: overlapping group sparsity with L1-norm; OGS-Lp: overlapping group sparsity with pseudo-norm. \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}{*}{House} & ATV & 32.31 & 0.8871 & 3.09 \\  & ITV & 31.81 & 0.8888 & **1.84** \\  & TGV & 32.32 & 0.9135 & 8.38 \\  & OGS-L1 & 33.04 & 0.9127 & 15.27 \\  & OGS-Lp & 37.47 & 0.9667 & 12.45 \\  & OGS-Lp-FAST & **37.72** & **0.9679** & 10.48 \\  & ATV & 35.32 & 0.9636 & 5.42 \\  & ITV & 35.33 & 0.9649 & **3.56** \\  & TGV & 35.29 & 0.9256 & 14.98 \\ \multirow{2}{*}{Shoulder} & OGS-L1 & 37.00 & 0.9719 & 17.77 \\  & OGS-Lp & 38.89 & 0.9829 & 16.06 \\  & OGS-Lp-FAST & **38.92** & **0.983** & 14.61 \\  & ATV & 29.45 & 0.8868 & 4.53 \\  & ITV & 30.05 & 0.894 & **3.22** \\ Girl & TGV & 30.14 & 0.8907 & 9.58 \\  & OGS-L1 & 30.67 & 0.8995 & 13.48 \\  & OGS-Lp & 32.29 & 0.9365 & 14.17 \\  & OGS-Lp-FAST & **32.34** & **0.9371** & 11.69 \\  & ATV & 32.32 & 0.8973 & 4.83 \\  & ITV & 31.02 & 0.9039 & **3.63** \\ Milk Drop & TGV & 30.48 & 0.894 & 8.59 \\  & OGS-L1 & 33.35 & 0.911 & 16.39 \\  & OGS-Lp & 35.76 & 0.9533 & 13.42 \\  & OGS-Lp-FAST & **35.87** & **0.9538** & 8.58 \\  & ATV & 29.45 & 0.8868 & 4.53 \\  & ITV & 29.65 & 0.9015 & **3.73** \\  & TGV & 29.84 & 0.8853 & 8.98 \\ Woman & OGS-L1 & 30.35 & 0.908 & 16.03 \\  & OGS-Lp & **31.71** & **0.9395** & 16.22 \\  & OGS-Lp-FAST & 31.7 & 0.9398 & 8.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \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{3-5}  & & & **PSNR (dB)** & **SSIM** & **Time (s)** \\ \hline \multirow{8}{*}{House} & ATV & 27.08 & 0.829 & 5 \\  & ITV & 27.06 & 0.837 & **2.88** \\  & TGV & 27.23 & 0.8319 & 8.16 \\  & OGS-L1 & 27.59 & 0.8543 & 9.56 \\  & OGS-Lp & 29.19 & 0.9035 & 15.72 \\  & OGS-Lp-FAST & **29.21** & **0.9039** & 7.48 \\  & ATV & 30.4 & 0.8717 & 3.8 \\  & ITV & 30.16 & 0.8545 & **2.17** \\
30\% & House & TGV & 30.82 & 0.8862 & 11.61 \\  & OGS-L1 & 31.5 & 0.8807 & 7.77 \\  & OGS-Lp & 35.03 & 0.9432 & 13.8 \\  & OGS-Lp-FAST & **35.13** & **0.9444** & 10.77 \\  & ATV & 34.48 & 0.9551 & 4.95 \\  & ITV & 34.33 & 0.9556 & **3.94** \\  & TGV & 34.74 & 0.9493 & 22 \\ Shoulder & OGS-L1 & 35.34 & 0.9599 & 16.97 \\  & OGS-Lp & **36.5** & **0.9633** & 18.88 \\  & OGS-Lp-FAST & 36.47 & 0.9628 & 5.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Numerical comparison of our proposed method and other models (images are corrupted by impulse noise of 30%).