# An Overview on Deep Learning Techniques for Video Compressive Sensing
## 8 Conclusions  
In this review, after reformulating the compressive sensing paradigm, we have closely reviewed the fundamentals of image and video compresssive sensing. In addition, we analyzed the backbone deep learning based architectures for image and video CS in order to provide the CS community the essential background knowledge. Indeed, we classified different concepts of compressive sensing in general and image and video compressive sensing in particular into categories to facilitate their understanding. The methods have been analyzed in this review from different angles: network architecture, contribution,  
\begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Classification Type** & **Category** & **Traditional/DL** & **Algorithm’s Class** & **Examples** & **Advantages** & **Limitations** \\ \hline Modulation strategy & Video Snapshot Compressive Imag@ng & Traditional & Sparse based & Low-Cost Compressive Sensing for Color Video and Depth & & \\ \hline  & & TV based & TwST [40], GAP-TV [44] & & & \\ \hline  & & GMM & GMM (Off-line training) [43] & & & \\ \hline  & & Dictionary Learning & 3D K-SVD & & Good reconstruction quality, Fast algorithms, less GPU memory consumption (BIRNAT, MetaSCI-Net [83], MetaSCI-Net [82], MetaSCI-Net [84]) & & & Less flexible, Not robust to real data noise-huge memory consumption (BIRNAT, MetaSCI-Net [83], MetaSCI-Net) & & \\ \hline  & & Plug and Play & [48;74] & Good trade-off between accuracy and speed and flexibility & The training phase can be slow \\ \hline  & & End-to-End & E2E-CNN [48] & Fast algorithms & Low flexibility \\ \hline Single pixel Cameras & Traditional & \(l1\)-regularized approach & & Good quality & Slow \\ \hline  & & \(l2\)-regularized approach & & Fast & Less good quality \\ \hline  & DL & RNN based & [66] & Good reconstruction quality, & Huge computational time \\ \hline  & & CNN based & [67] & Faster training & Huge memory consumption \\ \hline  & & Auto-encoder based & [47] & & \\ \hline \end{tabular}
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Table 4: Different algorithms for video compressive sensing (Part 2).  
complexity and performance results. In the end, we have discussed the future research challenges of compressive sensing. In conclusion, compressing sensing is a promising research direction in order to optimize data gathering and processing. Although there have been great achievements in this field, there is still room for improvement in image and video compressive sensing using neural networks.  
Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing original draft preparation and visualization: W.S.; writing review and editing: W.S. and F.C.; supervision, project administration and funding acquisition: D.H., F.C., J.-P.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the sensors generation project of Nouvelle Aquitaine region (2018-IR50214).  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.