## 4 Experiment and Result Analysis
### Result Analysis
In addition, the CAE reconstructed image becomes indistinguishable at 1%. CBAM_ISTA-Net\({}^{+}\) enhances the ability of network to accurately recover important features of the image during reconstruction process, resulting in more details and sharper edges. This confirms the excellent performance of CBAM_ISTA-Net\({}^{+}\) in the field of image compressive sensing and reconstruction. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Methods**} & \multicolumn{8}{c}{**CS Ratio**} & \multicolumn{2}{c}{**Time**} \\ \cline{3-10}  & & **50\%** & **40\%** & **30\%** & **25\%** & **10\%** & **4\%** & **1\%** & **GPU** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} WT \\ spectrogram \\ \end{tabular} } & CAE & 21.82 & 21.70 & 21.68 & 21.61 & 21.37 & 20.36 & 16.46 & 0.0216 s \\  & ISTA-Net & 35.00 & 34.13 & 32.51 & 31.44 & 26.72 & 24.34 & 19.33 & 0.1072 s \\  & ISTA-Net\({}^{+}\) & 38.31 & 36.29 & 33.66 & 32.44 & 27.30 & 23.92 & 19.84 & 0.1119 s \\  & CBAM\_ISTA-Net\({}^{+}\) & 38.59 & 36.41 & 33.89 & 32.63 & 28.70 & 24.09 & 20.51 & 0.1185 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{8}{c}{**CS Ratio**} & \multicolumn{2}{c}{**Time**} \\ \cline{3-10}  & & **50\%** & **40\%** & **30\%** & **25\%** & **10\%** & **4\%** & **1\%** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Set11 \\ CBAM\_ISTA-Net\({}^{+}\) \\ \end{tabular} } & ISTA-Net [23] & 37.43 & 35.36 & 32.91 & 31.53 & 25.80 & 21.23 & 17.30 \\  & ISTA-Net\({}^{+}\) & 38.01 & 36.04 & 33.73 & 32.40 & 26.51 & 21.57 & 17.21 \\  & CBAM\_ISTA-Net\({}^{+}\) & 38.13 & 36.08 & 33.83 & 32.57 & 26.75 & 21.69 & 17.32 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} BSD68 \\ CBAM\_ISTA-Net\({}^{+}\) \\ \end{tabular} } & ISTA-Net [23] & 33.60 & 31.85 & 29.93 & 29.07 & 25.02 & 22.12 & 19.11 \\  & ISTA-Net\({}^{+}\) & 34.01 & 32.17 & 30.34 & 29.29 & 25.32 & 22.38 & 19.03 \\ \cline{1-1}  & CBAM\_ISTA-Net\({}^{+}\) & 34.12 & 32.27 & 30.39 & 29.36 & 25.45 & 22.42 & 19.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the average PSNR (dB) performance on Set11 and BSD68 datasets. Figure 9: PSNR of the three datasets in CAE, ISTA-Net, ISTA-Net\({}^{+}\), and CBAM_ISTA-Net\({}^{+}\). To verify the reconstruction performance of CBAM_ISTA-Net\({}^{+}\), Figure 12 shows the classification accuracy of the bird images and spectrograms reconstructed by CBAM_ISTA-Net\({}^{+}\) and ISTA_Net\({}^{+}\), as well as the classification accuracy of the original image and spectrograms versus the reconstructed images and spectrograms. It can be seen that there is no particular difference in the classification accuracy between the original and reconstructed images. The classification accuracy of the CBAM_ISTA-Net\({}^{+}\)- and ISTA_Net\({}^{+}\)-reconstructed bird images is 76%, which is lower than that of original image. Similarly, the classification accuracy of the CBAM_ISTA-Net\({}^{+}\)-reconstructed Mel spectrograms and WT spectrograms reaches 86.76% and 90.12%, which are only 0.73% and 0.45% lower than those of the original spectrograms, and 0.49% and 0.51% higher than the reconstructed spectrograms of ISTA_Net\({}^{+}\). This shows that the CBAM_ISTA-Net\({}^{+}\) method not only achieves high-quality compressive reconstruction, but also preserves the semantic information of image and spectrogram. The results of CBAM_ISTA-Net\({}^{+}\) show that it can perform pre-tasks for further classification tasks. Figure 11: Reconstruction of four reconstruction methods on WT spectrograms. Figure 10: Reconstruction of four reconstruction methods on bird images.