# Deep Compressed Sensing Generation Model for End-to-End Extreme Observation and Reconstruction
## 4 Experiments and Results
### Fashion_MNIST Experiment and Analysis
The best performance was the combination of the deconvolution generative model and deep observation model (ADT), with a reconstruction accuracy of 94.74% and an SSIM of 0.7405. It can also be concluded from the accuracy and SSIM curve that the combination of the deconvolution generative model and deep observation model (ADT) is optimal. In summary, the combination of the deconvolution generative model and the deep observation model has a good reconstruction effect. Figure 7: Reconstruction accuracy (**a**) and SSIM (**b**) curves of different model combinations. The reconstruction effects are shown in Figures 8, 11, 14 and 17. The first row of all figures is the end-to-end reconstruction result of the MLP generative model, and the second row is the end-to-end reconstruction result of the Deconv_Net generative model. It can be seen intuitively that the accuracy and SSIM of the reconstructed image are relatively high in the two modes at lower compression ratios. The reconstructed image with a 90% compression ratio is shown in Figure 8. Columns one, three, and five in Figure 8 are the original images, and columns two, four, and six are the restored images of different models. In Figure 8, the details of the reconstructed image and the original image can be noticed from the red marked boxes. The reconstructed image contains many details of the original image. Figure 9 is a histogram of reconstruction accuracy and SSIM of different models when the compression rate is 90%. It can be seen that there is not much difference. Figure 10 shows the loss curves for different models. Through the loss curve, it is found that there is no significant difference in the value of reconstruction loss after observation using A, AT, and ADT. However, Deconv_Net's loss curve was more stable than MLP's. This also shows that Deconv_Net generalization ability is better than MLP for reconstructing all datasets. The reconstructed image with a compression ratio of 95% is shown in Figure 11. Columns one, three, and five in Figure 11 are the original images, and columns two, four, and six are the restored images of different models. In Figure 11, the details of the reconstructed image and the original image can be noticed from the red marked boxes. The reconstructed image contains many details of the original image. Figure 12 is a histogram of reconstruction accuracy and SSIM for different models when the compression rate is 95%. It can be seen that there is not much difference in the reconstruction accuracy, but the SSIM of Deconv is higher than that of MLP. Figure 13 shows the loss curves for different models. Similarly, through the loss curve, it is found that there is no significant difference in the value of reconstruction loss after observation using A, AT, and ADT. However, Deconv_Net's loss curve was more stable than MLP's. This also shows that Deconv_Net generalization ability is better than MLP for reconstructing all datasets. Figure 10: The joint loss curve with the end-to-end MLP model (**a**) and with the end-to-end Deconv_Net model (**b**) at a 90% compression ratio. The reconstructed image with a compression ratio of 98% is shown in Figure 14. Columns one, three, and five in Figure 14 are the original images, and columns two, four, and six are the restored images of different models. In Figure 14, the details and differences between the reconstructed and original images can be seen in the marked red boxes. The details of the reconstructed image become slightly blurred. Figure 14: Reconstruction results of at a 98% compression ratio. Figure 12: Comparison of (**a**) reconstruction accuracy and SSIM (**b**) between MLP and Deconv at a compression ratio of 95%. Figure 13: The joint loss curve with the end-to-end MLP model (**a**) and the end-to-end Deconv_Net model (**b**) at a 95% compression ratio. Figure 15 is a histogram of the reconstruction accuracy of different models when the compression rate is 98%. It can be seen that the reconstruction accuracy and SSIM of Deconv are slightly higher than those of MLP.