# Deep Compressed Sensing Generation Model for End-to-End Extreme Observation and Reconstruction
## 4 Experiments and Results
### MNIST Experiment and Analysis
As shown in Table 7, the compression ratio was [(\(784-4\))/\(784\)] \(\times\) 100% \(\approx\) 99.5%. When the observation dimension was 4 with the end-to-end method, the reconstruction accuracy of ADT was 91.61%, which was 2.07% better than AT and 4.54% better than A. The SSIM of ADT was 0.7032, which was 0.1185 higher than AT and 0.2515 higher than A. When random input of dimension 4 was used, the reconstruction accuracy of ADT was 83.86%, the reconstruction accuracy of AT was 84.95%, and the reconstruction accuracy of A was 83.80%. Their SSIM were all below 0.4. As shown in Figure 4, it can be seen intuitively that the end-to-end reconstruction accuracy and SSIM are better those that of random input. Figure 5 shows the training loss curves of the end-to-end method and random input; the end-to-end method's loss curve is significantly lower than that of random input. As shown in the average loss in Table 8, the average difference in numerical values is 22.2930. As shown in Figure 6, the first line is the reconstruction effect of random input with dimension 4, and the reconstruction effect of extreme observation is blurry and unsharp. The second line is the end-to-end reconstruction effect. From the perspective of the reconstruction image, the reconstructed image after the observation of A is blurry, and the same image contains many contours of other images, which are almost unrecognizable. Although the image reconstructed after using AT is blurry, the digit can be distinguished compared with the A reconstruction, and some reconstruction effects are still blurry. Compared with the A and AT reconstructions, the reconstructed image using ADT has a high definition. Figure 4: (Comparison of reconstruction accuracy (**a**) and SSIM (**b**) between random input and end-to-end. Figure 5: The joint loss curves for (**a**) random input and (**b**) end-to-end. The convergence response of the end-to-end method was very rapid, and the clearer and identifiable images were reconstructed almost in about 15 epochs, and the loss of ADT reconstruction was lower than that of AT and A. Figures 3 and 6 also proved the ADT reconstruction effect is better than that of AT and A. In Figure 6, the details and differences between the reconstructed and original images can be seen in the marked red boxes. In the experiment in this section, end-to-end was used in the reconstruction experiment of MNIST. The compression ratio of 85% was the boundary. When the compression ratio was lower than 85%, the reconstruction effect was similar to that of A, AT, and ADT under the condition that MLP was used as the generative model. However, compared with AT and ADT, A had less computation and more flexible code operations. In the case of similar reconstruction results, the low computational cost shows apparent advantages. When the compression ratio was higher than 85%, the reconstruction effect of ADT was significantly better than that of AT and A and only needed about 15 epochs for rough reconstruction. In contrast, the higher accuracy of reconstruction in a short time shows an advantage over the lower-dimensional observation reconstruction. With the compression ratio at 99.5% of the extreme observation, the reconstruction accuracy of the end-to-end method improved by 5.20% on average compared with the random input of DCS, and the SSIM improved by 0.22 on average. That verified the feasibility of the extreme observation and reconstruction method and showed that the end-to-end reconstruction method had excellent elasticity in the observation dimension.