# Deep Compressed Sensing Generation Model for End-to-End Extreme Observation and Reconstruction
## 4 Experiments and Results
### MNIST Experiment and Analysis  
The experiments in this section mainly verify the feasibility of our proposed end-to-end correspondence relationship during the reconstruction process. Therefore, we used the same MLP generative model as DCS in all the reconstruction processes of the experiments in this section. In contrast to DCS's random input, we used end-to-end to establish correspondence. This is fixing the input. In the reconstruction experiment of the MNIST dataset, the observed original data was 28 \(\times\) 28. A, AT, and ADT were used as observation models. And the MLP was used as a generative model with the scale of sensing_dim-256-512-784. Reconstruction was performed at compression ratios of 75%, 80%, 85%, 90%, 95%, 98%, and 99.5% (the observation dimensions were 196, 157, 118, 78, 39, 16, and 4 (extreme observation)). Figure 2 shows the reconstructed accuracy curve and SSIM curve under different compression ratios using the method we proposed for end-to-end correspondence establishment. MLP is the generation model, and A, AT, and ADT are three observation models. It can be seen that the compression ratio of 85% is the boundary. When the compression ratio is lower than 85%, the reconstruction effect after observation using A, AT, and ADT is similar. When the compression ratio is above 85%, the ADT reconstruction effect is better than that of AT and A. Table 7 shows more detailed values. It can be seen that reconstruction accuracy is higher than 99% and SSIM is higher than 0.90 within the 85% compression rate. When the compression ratio was 90% (the observation dimension was 78), the reconstruction accuracy of ADT observation was 99.6%, which was similar to the reconstruction effect of AT, and 0.55% higher than that of A. Moreover, the SSIM of ADT was 0.9817, which was 0.0042 higher than AT and 0.06 higher than A. When the compression ratio was 95% (the observation dimension was 39), the reconstruction accuracy of ADT was 99.48%, which was 0.21% higher than AT and 1.52% higher than A. Furthermore, the SSIM of ADT was 0.9775, which was 0.0116 higher than AT and 0.3778 higher than A. When the compression ratio was 98% (the observation dimension was 16), the reconstruction accuracy of ADT was 98.45%, which was 0.54% higher than AT and 4.61% higher than A. The SSIM of ADT was  
Figure 2: (**a**) Reconstruction accuracy curves of different model combinations; (**b**) Reconstruction SSIM curves of different model combinations. 0.9353, which was 0.0345 higher than AT and 0.3452 higher than A. Figure 3 shows the reconstruction results of our proposed method. The details of the reconstructed image and the original image can be seen in the marked red boxes. The high reconstruction accuracy and reconstruction details demonstrate the feasibility of the proposed method. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Compression Ratio** & \multirow{2}{*}{**75\% (196)**} & \multirow{2}{*}{**80\% (157)**} & \multirow{2}{*}{**85\% (118)**} & \multirow{2}{*}{**90\% (78)**} & \multirow{2}{*}{**95\% (39)**} & \multirow{2}{*}{**98\% (16)**} \\ \cline{3-3} **(Number of** & & & & & & & & & & & \\ \cline{3-3} **Observations)** & **Accuracy** & **SSIM** & **Accuracy** & **SSIM** & **Accuracy** & **SSIM** & **Accuracy** & **SSIM** & **Accuracy** & **SSIM** \\ \hline A & 99.76\% & 0.9746 & 99.61\% & 0.9710 & 99.54\% & 0.9701 & 99.05\% & 0.9217 & 97.96\% & 0.5997 & 93.82\% & 0.5901 \\ AT & **99.76\%** & **0.9829** & 99.67\% & 0.9731 & 99.68\% & 0.9784 & 99.60\% & 0.9775 & 99.27\% & 0.9659 & 97.91\% & 0.9008 \\ ADT & 99.62\% & 0.9794 & **99.72\%** & **0.9868** & **99.75\%** & **0.9859** & **99.60\%** & **0.9817** & **99.48\%** & **0.9775** & **98.45\%** & **0.9353** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of MNIST reconstruction accuracy and SSIM. Figure 3: Experimental results of MNIST reconstruction. To further research the efficacy of the proposed method, we continue to study the reconstruction of extreme observations.