# Deep Compressed Sensing Generation Model for End-to-End Extreme Observation and Reconstruction
## 4 Experiments and Results
### Fashion_MNIST Experiment and Analysis  
In the experiments in the previous section, the feasibility of our proposed end-to-end correspondence relationship for reconstruction was verified. The end-to-end reconstruction is better than random input. However, to further improve the reconstruction effect, the structure of the generative model has become our focus. Although the original data in the S-REC condition are free from the sparsity requirement, the handwritten digits of MNIST have noticeable foreground and background, \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Observation Model**} & \multicolumn{1}{c}{**A**} & \multicolumn{1}{c}{**AT**} & \multicolumn{1}{c}{**ADT**} \\ \hline \multirow{2}{*}{Reconstruction accuracy} & random input & 83.80\% & 84.95\% & 83.86\% \\  & end-to-end input & 87.07\% & 89.54\% & 91.61\% \\ \hline \multirow{2}{*}{SSIM} & random input & 0.3491 & 0.3501 & 0.3803 \\  & end-to-end input & 0.4517 & 0.5847 & 0.7032 \\ \hline \multirow{2}{*}{Average loss} & random input & 70.3399 & 53.0402 & 53.2837 \\  & end-to-end input & 47.4702 & 33.8370 & 28.4777 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of reconstruction results for extreme observations. Figure 6: Comparison of reconstructed images of extreme observations. making the MNIST dataset have inherent sparsity. Therefore, the Fashion_MNIST dataset was used in this section. In the end-to-end reconstruction experiment of the Fashion_MNIST dataset, the observed original data was 28 \(\times\) 28. The observation models used were A, AT, and ADT. The generative model of the experimental group was a Deconv_Net, and the control group was an MLP in DCS. According to the experimental results in Section 4.4, when the compression ratio was lower than 85%, the AT and ADT reconstruction advantage was not as good as that of A. Therefore, only the reconstruction of the low observation dimension was performed in this section, and the reconstruction was performed at the compression ratios of 90%, 95%, 98%, and 99.5% (the observation dimension was 78, 39, 16, and 4 (extreme observation)). As shown in Figure 7, reconstruction accuracy curves and SSIM curves of different model combinations under different compression ratios are obtained. It can be seen from the trend that the reconstruction accuracy and SSIM of Deconv_Net+ADT are at high levels under different compression ratios. The reconstruction accuracy and SSIM of MLP+A are lower under different compression ratios. Table 9 shows more detailed values. When the compression ratio was 90% (the observation dimension was 78), the reconstruction accuracy of the Deconv_Net was 3% higher than that of the MLP on average under different observation models, and the SSIM was 0.1797 higher than that. The best performance was the combination of the deconvolution generative model and the deep observation model (ADT), with a reconstruction accuracy of 99.23% and an SSIM of 0.9427. When the compression ratio was 95% (the observation dimension was 39), the reconstruction accuracy of the Deconv_Net was 2.74% higher than the MLP on average, and the SSIM was 0.1521 higher than that. The best performance was the combination of the deconvolution generative model and the deep observation model (ADT), with a reconstruction accuracy of 98.67% and an SSIM of 0.9152. When the compression ratio was 98% (the observation dimension was 16), the reconstruction accuracy of the Deconv_Net was 3.34% higher than the MLP on average, and the SSIM was 0.1346 higher than that. The best performance was the combination of the deconvolution generative model and the deep observation model (ADT), with a reconstruction accuracy of best performance was the combination of the deconvolution generative model and the deep observation model (ADT), with a reconstruction accuracy of 98.00% and an SSIM of 0.8618. When the compression ratio was 99.5% (the observation dimension was 4), the reconstruction accuracy of the Deconv_Net was 2.49% higher than the MLP on average, and the SSIM was 0.1595 higher than that.