## 4 Conclusions  
Deep learning has achieved good results in many fields; this paper organically combines deep learning with image fusion, and proposes an image fusion algorithm based on deep learning. Tables 4-6 show that our model has achieved good results on various datasets. The multi-branch, multi-scale deep learning image network model proposed in this paper extracts image features by adding multiple branches and introducing the DenseBlocks module in the design of the encoder. The activation function is not used after each convolution in the entire network, and in the selection of activation functions, this article does not use all of them one activation function but uses multiple activation functions. In the design of the loss function, we use the SSIM values between images as a loss function for the network. In the design of the decoder, we use Dense Blocks for dimensionality reduction processing of images. The model proposed in this paper has largely preserved the original image after the fusion and is very realistic and natural. Experiments have proved that the network model proposed in this paper has achieved the best results on most of the datasets. In the future, we will try more methods of image fusion and study how to reduce the super parameters of the network. Following this, we will improve the network efficiency and strive to solve more problems in the field of image fusion. Conceptualization, Z.C. ; methodology, Z.C. ; software, Z.C. ; validation, Y.D., Z.C., F.G. and Z.L. ; formal analysis, Z.C. ; data curation, Z.C. and Z.L. ; writing--original draft preparation, Z.C. ; writing--review and editing, Z.C. and Y.D. ; visualization, Z.C. and Z.L. ; supervision, Y.D. ; funding acquisition, Y.D. and F.G. All authors have read and agreed to the published version of the manuscript. This research was funded by he National Natural Science Foundation of China grant No. 61772295, 61572270, the PHD foundation of Chongqing Normal University (No. 19 XLB003), the Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD.M202-000501), Chongqing Technology Innovation and Application Development Special General Project (cstc-2020 jscylysAX0002) and Chongqing Technology Foresight and Institutional Innovation Project (cstc2021-jsyj-yzzys-BAX0011). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **SF** & **EN** & **Q\({}^{abf}\)** & **SSIM** & **MS-SSIM** & \(N^{abf}\) & **MI** & **VIF** & **SD** & **DF** \\ \hline Exposure & 23.57095 & 6.82891 & 0.39976 & 0.52032 & 0.83796 & 0.11205 & 13.65782 & 2.76957 & 55.54952 & 9.43259 \\ Exposure & 23.57091 & 0.3991 & 0.3991 & 0.3991 & 0.3991 & 0.3991 & 0.3991 & 0.3991 & 2.39959 & 53.42360 & 1.12997 \\ \hline Road & 11.44829 & 6.88796 & 0.39165 & 0.69968 & 0.85715 & 0.00744 & 13.77593 & 0.43175 & 34.13524 & 5.24988 \\ Road & 15.8838 & 0.3917 & 0.4918 & 0.66609 & 0.71187 & 0.16426 & 0.66715 & 0.7255 & 0.73111 & 0.72558 \\ \hline Two & 8.57529 & 6.32894 & 0.34281 & 0.71874 & 0.85294 & 0.00911 & 12.65789 & 0.35355 & 27.28184 & 3.89286 \\ \hline Track & 6.144829 & 0.71884 & 0.5518 & 0.70152 & 0.66616 & 0.66166 & 0.66616 & 0.66616 & 0.66616 & 0.66616 \\ \hline \hline \end{tabular}  
* The network model proposed in this paper.  
\end{table} 
Table 6: The result of ablation experiments. Data Availability Statement: The data we use can be obtained from GitHub, [[https://github.com/thflylsty/ImageFusion_DeepDecFusion/tree/main/testdata](https://github.com/thflylsty/ImageFusion_DeepDecFusion/tree/main/testdata)]([https://github.com/thflylsty/ImageFusion_DeepDecFusion/tree/main/testdata](https://github.com/thflylsty/ImageFusion_DeepDecFusion/tree/main/testdata)), Our code: [[https://github.com/Cevev/MDPI-Applsci](https://github.com/Cevev/MDPI-Applsci)]([https://github.com/[PERSON]/MDPI-Applsci](https://github.com/Cevev/MDPI-Applsci)).