# Deep Compressed Sensing Generation Model for End-to-End Extreme Observation and Reconstruction
## 3 Method
### Model Structure  
The overall model structure of end-to-end deep compressed sensing using generative models (E2E_DCSGM) is shown in Figure 1. In the inner loop, the input of the generative model was fine-tuned to obtain the minimum loss of the inner loop. In the outer loop, joint loss optimized the generative model and the observation model. The reconstruction process was accelerated by the idea of inner and outer bidirectional optimization. Figure 1: Block diagram to the E2E_DCSGM structure. Deconv_Net and MLP were used as generative models. Untrainable matrix (A), trainable matrix (AT), and multi-layer perceptron observation network (ADT) were used as observation models. The experimental group used a deconvolution generative model and three observation models. Moreover, the control group was DCS, a combination of the multi-layer perceptron and three observation models. The details are shown in Table 2.  
The experimental part adopts MLP and Deconv_Net as the generators. The straightening process of MLP destroys the spatial characteristics of the image itself. A convolutional network is a hierarchical feature-bearing model. Each layer's feature map can contain the training images' spatial features. During the training process, the feature map of the number of channels in each layer learns the spatial characteristics of the training images. These feature maps become prior knowledge during the training process for the next epoch. This is similar to translation invariance. Although MLP is easier to overfit than Deconv_Net, it is necessary to fit all data sets as much as possible while fitting a single data. In other words, the reconstruction process requires overfitting and generalization of the entire dataset. MLP needs a vast network structure to meet the above conditions. The feature map of each layer of Deconv_Net can meet the above conditions. Table 3 shows the hyperparameter settings of the multi-layer perceptron generative model. The structure was sensing_dim-128-256-512-784, except that the activation function of the last layer was Tanh, the activation function of each layer was LeakyReLU, and the input of the generative model was determined by the observation dimension (sensing_dim). Table 4 shows the hyperparameter settings of the deconvolution generation model. The input side was adjusted to 256 channels, the feature map size was 7 \(\times\) 7, and the feature map was expanded to 14 \(\times\) 14 after upsampling 1. The deconvolution layer 1 had 128 channels, the convolution kernel size was 3 \(\times\) 3, the stride size was 1, and the padding size was 1. The feature map of 128 channels (14 \(\times\) 14) was obtained. After upsampling 2, the feature map was expanded to 28 \(\times\) 28. The deconvolution layer 2 had 64 channels, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Input** & **Models** & **Type** & **Purpose** \\ \hline CSGM & Random input & MLP + A & Control Group & \\ DCS & Random input & MLP + AT & Control Group & Random input control group \\ \hline \multirow{4}{*}{We proposed} & end-to-end & MLP + AT & \multirow{2}{*}{Experimental Group} & \multirow{2}{*}{Verify the feasibility of end-to-end} \\  & & MLP + ADT & & \\ \cline{1-1} \cline{3-4}  & & Deconv\_Net + A & & \\ \cline{1-1} \cline{3-4}  & end-to-end & Deconv\_Net + AT & \multirow{2}{*}{Experimental Group} & \multirow{2}{*}{Verify the reconstruction effect of the improved generator on the extreme observation under the end-to-end case} \\  & & Deconv\_Net + ADT & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model combination of experimental and control groups.