# Deep Compressed Sensing Generation Model for End-to-End Extreme Observation and Reconstruction
## 1 Introduction  
Nowadays, people cannot live without digitalization. In this era of digitalization, the primary problem is the storage and transmission of massive amounts of data. In the early days, the process of digital acquisition of analog signals was inseparable from the traditional Nyquist-Shannon sampling theorem, which points out that the sampling frequency must be more than twice the highest frequency of the original signal in order to completely retain the information in the original signal or accurately reconstruct the original signal [1]. However, in the digital age, where the demand for information is surging, the Nyquist-Shannon sampling theorem causes redundant sampling, even with the rapid development of current computer technology. The sampling rate, storage space, transmission bandwidth, and processing speed for huge amounts of data can consume huge resources, and the ways to solve these problems are pointed toward the compression technology of the signal. In 2006, [PERSON] et al. proposed the theory of Compressed sensing [2] (CS), which is different from traditional Nyquist sampling in that the signal could be sampled at much lower frequencies than Nyquist sampling, resulting in less redundancy in the sampled data and complete reconstruction of the original signal with high probability [3]. Compressed sensing has been applied in many aspects [4]. For example, in wireless communications, channel estimation technology based on compressed sensing has been proposed to improve spectral efficiency [5] and the efficiency of channel estimation at multi-sensor nodes [6]. In medical images, compressed sensing has been proposed for incoherent undersampling and efficient reconstruction of MR Images [7], the application for Photo-acoustic (PA)tomography [8], the K-t focus to new dynamic MRI has been proposed from compressed sensing [9], the application of compressed sensing to high-resolution 3D upper airway MRI [10], and taking advantage of the high SNR from hyperpolarization achieving a factor of two spatial resolution enhancement for 3D MRSI [11]; In terms of radar measurement, a compressed sensing approach has been proposed for the target scene reconstruction and has a higher resolution than classical radar [12]. In astronomical imaging, compressed sensing has been proposed to compress redundant astronomical data effectively [13]. In defect detection of the integrated circuit, compressed sensing has been proposed to estimate chip leakage tomography quickly and accurately [14]; In terms of pattern recognition, the sparse representation of compressed sensing has been used to enhance feature extraction for face recognition [15]. The optimization of compressed sensing projection has been considered to obtain better reconstruction performance [16]. Compressed sensing and machine learning have proven the feasibility of learning directly in the compressed domain [17]. And the proposal for single-pixel cameras [18], etc. The common point of the above applications is the need to observe and reconstruct signals. However, an important premise of traditional compressed sensing is the sparsity of signals, which is a rather harsh condition limiting compressed sensing. In nature, not all signals have sparse transformation domains. Even if sparse transformations are performed with the help of sparse priors, the effect of inefficient reconstruction will be produced. In recent years, with the rapid development of deep learning, the mechanisms of training, fitting, and discriminating decisions on data have made breakthroughs in both academic and industrial fields. Deep learning is a machine learning method that learns complex dataset mappings through modified weights in the hidden layers of a network. The deeper the network, the more parameters there are in the hidden layers of that network, and the greater the capacity to carry features in the network, making the deep networks stronger to learn. Researchers used deep learning to optimize compressed sensing to solve the problem. In March 2017, [PERSON] et al.