# Deep Compressed Sensing Generation Model for End-to-End Extreme Observation and Reconstruction
## 4 Experiments and Results
### Fashion_MNIST Experiment and Analysis
Figure 16 is the loss curve for different models. When MLP is the generation model, the reconstructed loss curve after observation with A, AT, and ADT fluctuates greatly and is very unstable. The loss curve of Deconv_Net was more stable than that of MLP. This suggests that Deconv_Net has been shown to generalize the fitted datasets better than MLP as the compression ratio increases. The reconstructed image with a compression ratio of 99.5% is shown in Figure 6:0 Columns one, three, and five in Figure 17 are the original images, and columns two, four, and six are the restored images of different models. In Figure 17, the details and differences between the reconstructed and original images can be seen in the marked red boxes. The details of the reconstructed image of A become blurred, while AT and ADT still maintain contours and details. Figure 18 is a histogram of the reconstruction accuracy for different models when the compression rate is 99.5%. It can be seen that the reconstruction accuracy and SSIM of Deconv are significantly higher than those of MLP. Figure 6:1 The joint loss curve with the end-to-end MLP model (**a**) and the end-to-end Deconv_Net model (**b**) at a compression ratio of 98%. Figure 6:2 Comparison of reconstruction accuracy (**a**) and SSIM (**b**) between MLP and Deconv at a compression ratio of 98%. Figure 19 is the loss curve for different models. When MLP is the generation model, the reconstructed loss curve after observation with A, AT, and ADT has a significant fluctuation and is very unstable. The loss curve of Deconv_Net was more stable than that of MLP. It can be seen from the loss curves of both models that the loss curves observed by ADT are significantly lower than those of A and AT. Again, Deconv_Net shows a better generalization of all fitted datasets than MLP as the compression ratio increases. Figure 6:3 Reconstruction results of at a 99.5% compression ratio. Figure 6:4 Comparison of reconstruction accuracy (**a**) between MLP and Deconv and SSIM (**b**) at a compression ratio of 99.5%. In the extreme observation a the dimension of four, the loss tends to be stable over a small number of epochs, and a relatively clear reconstruction image is obtained. Moreover, the loss curve of the Deconv_Net is significantly more stable than that of the MLP. It indicates the relative stability of the end-to-end correspondence. This section also concludes that the higher the number of observation dimensions, the closer the reconstruction accuracy of A, AT, and ADT. The more no difference between the reconstruction effect and the reconstruction accuracy can be guaranteed above 96%. In this case, the reconstruction method combining A with the generative model with a lower cost can be selected. As the observation dimension becomes smaller and smaller, the observation vector seems to \"condense\" the original signal, and a minimal amount of observation signal carries a massive amount of information about the original signal. In the reconstruction experiments of different observation models, the reconstruction accuracy of the Deconv_Net generative model is 1.38% higher than that of the MLP generation model of DCS, and the SSIM is 0.0522 points higher than that. When the extreme observation with a compression ratio is 99.5% (the observation dimension is 4), the reconstruction accuracy of the Deconv_Net generative model is 2.49% higher than that of the MLP generative model of DCS, and the SSIM is 0.0532 higher than that. It also shows that the combination of the deconvolution generative model and the multi-layer perceptron observation model provides a higher possibility for the high-precision reconstruction of the observed image by establishing the corresponding relationship from end-to-end.