# Blind Image Separation Method Based on Cascade Generative Adversarial Networks
## 4 Discussion  
In this article, we proposed a BIS method based on cascaded GANs that can perform the image separation task without multiple prior constraints. This method uses the UGAN to learn image mixing, which solves the problem of unpaired samples in the training  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PSNR** & **CAP** & **GDCP** & **MOF** & **GCANet** & **Ours** \\ \hline RICE-I & 24.51/0.82 & 20.35/0.83 & 16.64/0.73 & 19.93/0.80 & 25.92/0.85 \\ RICE-II & 20.97/0.61 & 17.18/0.54 & 18.04/0.48 & 19.16/0.56 & 21.15/0.82 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Remote sensing image results (PSNR, SSIM).  
Figure 6: Results of the RICE-I dataset for image dehazing.  
Figure 7: Results of the RICE-II dataset for cloud removal.  
process; the PAGAN is used to learn image separation. The PAGAN module adopts a self-attention mechanism to implement complex geometric constraints on the global image structure more accurately. The proposed method is suitable for different scenes, and extensive experiments demonstrate that it is able to provide competitive and high-quality separation results for both natural images and remote sensing images. In the future, we will continue to explore a unified framework that is more suitable for single-channel BIS and further expand the applicability and portability of this method.  
Formal analysis, F.J., X.S. and Y.M.; methodology, X.S., F.J. and Y.M.; resources, J.X.; data curation, X.S. and F.J.; writing--original draft preparation, F.J. and X.S.; writing--review and editing, J.X. and M.N.; supervision, J.X. and M.N.; funding acquisition, J.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China, grant number 62072391 and 62066013; the Natural Science Foundation of Shandong, grant number ZR2019 MF060; A Project of Shandong Province Higher Educational Science and Technology Key Program, grant number J18 KZ016, and the Graduate Science and Technology Innovation Fund Project of Yantai University, grant number YDYB2122.  
Not applicable.  
Publicly available datasets were analyzed in this study. The data can be found here: [[[https://github.com/BUPTLdy/RICE_DATASET](https://github.com/BUPTLdy/RICE_DATASET)]([https://github.com/BUPTLdy/RICE_DATASET](https://github.com/BUPTLdy/RICE_DATASET)), [[http://vision.cs.utexas.edu/projects/finegrained/utzap50k/](http://vision.cs.utexas.edu/projects/finegrained/utzap50k/)]([http://vision.cs.utexas.edu/projects/finegrained/utzap50k/](http://vision.cs.utexas.edu/projects/finegrained/utzap50k/)) and [[https://github.com/junyanz/iGAN#igan-interactive-image-generation-via-generative-adversarial-networks](https://github.com/junyanz/iGAN#igan-interactive-image-generation-via-generative-adversarial-networks)]([https://github.com/junyanz/iGAN#igan-interactive-image-generation-via-generative-adversarial-networks](https://github.com/junyanz/iGAN#igan-interactive-image-generation-via-generative-adversarial-networks))] accessed on 8 September 2021.  
The authors declare no conflict of interest.