# Deep Compressed Sensing Generation Model for End-to-End Extreme Observation and Reconstruction
## 5 Conclusions  
First of all, for the reconstruction of extreme observations, the end-to-end correspondence method proposed by us shows a very obvious reconstruction effect in MNIST dataset experiments compared with the random input of CSGM and DCS. The reason is that it is difficult to find random variables in the latent space that match the corresponding reconstructions during the reconstruction of extreme observations. Our idea is to directly use the low-dimensional vector of extreme observation to correspond with the observed original data for reconstruction. RIP and S-REC provide a guarantee for establishing the corresponding relationship. Secondly, in the end-to-end framework, the method of us combining the deconvolution generative model with the observation model is compared with the method of DCS combining the multi-layer perceptron generative model with the observation model. The reconstruction accuracy is improved by 1.38%, and the SSIM is improved by 0.0522 on average. The reconstruction accuracy of the extreme observation is improved by 2.49%, and the SSIM is improved by 0.0532 on average. The deep observation model of a multi-layer perceptron and the deconvolution generation model show excellent reconstruction performance. Since, in the deep observation model of a multi-layer perceptron, the dimension reduction of each hidden layer is constrained by RIP, different original signals will not be mapped to the same observation vector. At the same time, the generative model is also subject to RIP constraints: different observation vectors will not map to the same reconstruction signal, which provides a guarantee for the reconstruction of extreme observations. In the hidden layer structure of the generative model, the deconvolution generation model is different from the multilayer perceptron. The feature map of the deconvolution network carries the spatial information of the image, so in the extreme, the feature map of the hidden layer of the convolution network carries more data features than the linear connection of the multilayer perceptron, which is beneficial to the reconstruction of image data.  
Generalization ability is commonly mentioned in machine learning, and overfitting is a key obstacle in machine learning that is not easy to overcome. In this paper, the generative neural network model was used to reconstruct compressed sensing, which was a lossy reconstruction process. The reconstruction process precisely uses the disadvantage of overfitting to improve the reconstruction's accuracy. In future research, we will continue exploring methods for the high-precision reconstruction of low-dimensional observations and combine this method with application scenarios.  
**Author Contributions:** Conceptualization, X.L.; methodology, H.D.; software, H.D.; validation, H.D. and C.F.; formal analysis, H.D.; investigation, H.D.; resources, X.L.; data curation, X.L.; writing--original draft preparation, H.D.; writing--review and editing, C.F.; visualization, C.F.; supervision, X.L. and C.F.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Cross-Disciplinary Science Foundation from Beijing Institute of Petrochemical Technology (Project No. BIPTCSF-21).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used in this study including the MNIST and Fashion_MNIST image sets are openly available.  
**Conflicts of Interest:** The authors declare no conflict of interest.