## 4 Experiment and Result Analysis
### Result Analysis  
In the experiments, all the results are taken from five independent trainings. For bird images and spectrograms, the reconstruction performance of CAE, ISTA-Net, ISTA-Net\({}^{+}\), and CBAM_ISTA-Net\({}^{+}\) were compared under different compression ratios. Table 2 lists the average reconstruction time of each image. It can be observed that the CBAM_ISTA-Net\({}^{+}\) outperforms the other networks at all compression ratios, indicating that CBAM_ISTA-Net\({}^{+}\) can better preserve the detailed information in images and spectrograms, thus improving the quality of reconstruction. At a compression ratio of 50%, the average PSNR of CBAM_ISTA-Net\({}^{+}\) for bird images, Mel spectrograms, and WT spectrograms reached 33.62 dB, 55.76 dB, and 38.59 dB, respectively, while the average PSNR of the convolutional autoencoder are only 21.58 dB, 23.13 dB, and 21.82 dB, respectively. The average reconstruction time for each image is less than 1 s, and when reconstructing the Mel spectrum, the reconstruction time is less than that of ISTA_Net\({}^{+}\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer** & **Name** & **Type** & **Kernel Size** & **Stride** & **Input Size** \\ \hline
1 & Conv Input & Input Layer & - & - & \(224\times 224\times 3\) \\
2 & Conv1 & Convolution2D & \(3\times 3\) & 1 & \(224\times 224\times 3\) \\
3 & Pool1 & MaxPool2D & \(2\times 2\) & 2 & \(112\times 112\times 64\) \\
4 & Conv2 & Convolution2D & \(3\times 3\) & 1 & \(112\times 112\times 64\) \\
5 & Pool2 & MaxPool2D & \(2\times 2\) & 2 & \(56\times 56\times 128\) \\
6 & - & Flatten & - & - & \(56\times 56\times 128\) \\
7 & Fc1 & Linear & - & - & \(128\times 56\times 56\) \\
8 & Fc2 & Linear & - & - & \(256\) \\
9 & - & Output & - & - & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: CNN model structural parameters. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Methods**} & \multicolumn{6}{c}{**CS Ratio**} & \multicolumn{3}{c}{**Time**} \\ \cline{3-10}  & & **50\%** & **40\%** & **30\%** & **25\%** & **10\%** & **4\%** & **1\%** & **GPU** \\ \hline \multirow{4}{*}{Bird image} & CAE & 21.58 & 21.37 & 21.31 & 21.15 & 20.63 & 19.80 & 16.36 & 0.0039 s \\  & ISTA-Net & 32.36 & 30.27 & 28.20 & 27.58 & 22.74 & 20.65 & 17.15 & 0.054 s \\  & ISTA-Net\({}^{+}\) & 33.31 & 31.48 & 29.54 & 28.42 & 23.93 & 20.07 & 16.99 & 0.058 s \\  & CBAM_ISTA-Net\({}^{+}\) & 33.62 & 31.77 & 29.80 & 28.68 & 24.12 & 20.44 & 17.31 & 0.061 s \\ \hline \multirow{4}{*}{Mel spectrogram} & CAE & 23.13 & 22.92 & 22.51 & 22.41 & 21.64 & 20.65 & 17.29 & 0.0137 s \\  & ISTA-Net & 38.68 & 37.18 & 35.88 & 34.22 & 29.70 & 24.85 & 19.47 & 0.1046 s \\ \cline{1-1}  & ISTA-Net\({}^{+}\) & 47.00 & 41.15 & 39.89 & 37.76 & 29.14 & 24.20 & 19.69 & 0.1104 s \\ \cline{1-1}  & CBAM_ISTA-Net\({}^{+}\) & 55.76 & 53.58 & 46.27 & 40.40 & 29.67 & 24.68 & 19.84 & 0.079 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the average PSNR (dB) performance of various networks on bird images and spectrograms. Figure 9 shows the PSNR of the three datasets with the four different methods under different compression ratios. It is evident that CBAM_ISTA-Net\({}^{+}\) achieves the best reconstruction results under any of the compression ratios. To verify the performance of CBAM_ISTA-Net\({}^{+}\), the experiments are carried on datasets of the Set11 and BSD68 mentioned in [23]. Table 3 lists the average PSNR reconstruction performance for seven CS ratios on Set11 and BSD68. The results show that CBAM_ISTA-Net\({}^{+}\) achieves better reconstruction results, and is an improvement over the previous methods in almost all cases. The reconstructed images of bird images and audio wavelet transform spectrograms at different CS ratios are shown in Figures 10 and 11. From the figures, it can be seen that the ISTA-Net reconstructed images are clearer and more realistic, while the CAE reconstructed images are more blurred and distorted.