# RETRACTED: Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Networks
## 5 Conclusions  
This paper proposed the image super-resolution algorithm based on DCCNN. The deep channel was used to extract the detailed texture information of an image and increase the local receptive field of the image. The shallow channel was mainly used to restore the overall outline of the image. Experimental results showed that the simplified model parameters could not only enhance the ability of the network model to fit the model characteristics but also enable the network model to be trained at a higher learning rate, improving the model's convergence speed. At the same time, the long-term and short-term memory blocks constructed by the residual blocks in the network performed better than the single mapping output network using only the residual blocks. The quantity of image recovery was better, and the performance improved, which proves the necessity of using long-term and short-term memory blocks. Improvement could be observed in both subjective visual effect and objective evaluation parameters, as well as in efficiency, which proves the practicability of the proposed method.  
Conceptualization Y.C.; Methodology, J.W.; Software X.C.; Validation, A.K.S.; Formal Analysis, Y.C.; Investigation J.W.; Resources Z.C.; Data Curation, K.Y. and Z.C.; Supervision J.W. and A.K.S.; Funding Acquisition, [PERSON] and K.Y. Y.C. provided extensive support in the overall research; [PERSON] conceived and designed the presented idea, developed the theory, performed the simulations, and wrote the paper; [PERSON] and X.C. verified the analytical methods and encouraged to investigate various relevant aspects of the proposed research; A.K.S. and Z.C. provided critical feedback and helped shape the research, analysis, and manuscript. All authors discussed the results and contributed to the final manuscript.  
This research was funded by the National Natural Science Foundation of China [61811530332, 61811540410], the Open Research Fund of Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation [2015 TP1005], the Open Research Fund of Hunan Provincial Science and Technology Innovation Platform of Key Laboratory of Dongting Lake Aquatic Eco-Environmental Control and Restoration [2018 DT04], the Changsha Science and Technology Planning [KQ1703018, KQ1706064, KQ1703018-01, KQ1703018-04], the Research Foundation of Education Bureau of Hunan Province [17A007, 16A008], Changsha Industrial Science and Technology Commissioner [2017-7], the Junior Faculty Development Program Project of Changsha University of Science and Technology [2019 QJCZ011].  
We are grateful to our anonymous referees for their useful comments and suggestions. The authors also thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] for their useful advice during this work.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Feature** & \multirow{2}{*}{**Up-Sampling/ms**} & \multirow{2}{*}{**Reconstruction/ms**} & **Shallow** \\  & **Extraction/ms** & & & **Channel/ms** \\ \hline EEDS & 38,015 & **4112** & 154,834 & 7265 \\ DCCNN & **19,151** & 24,895 & **70,500** & **5231** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of computational complexity with phases.