RETRACTED: Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Networks
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Source abstract
Abstract
For the image super-resolution method from a single channel, it is difficult to achieve both fast convergence and high-quality texture restoration. By mitigating the weaknesses of existing methods, the present paper proposes an image super-resolution algorithm based on dual-channel convolutional neural networks (DCCNN). The novel structure of the network model was divided into a deep channel and a shallow channel. The deep channel was used to extract the detailed texture information from the original image, while the shallow channel was mainly used to recover the overall outline of the original image. Firstly, the residual block was adjusted in the feature extraction stage, and the nonlinear mapping ability of the network was enhanced. The feature mapping dimension was reduced, and the effective features of the image were obtained. In the up-sampling stage, the parameters of the deconvolutional kernel were adjusted, and high-frequency signal loss was decreased. The high-resolution feature space could be rebuilt recursively using long-term and short-term memory blocks during the reconstruction stage, further enhancing the recovery of texture information. Secondly, the convolutional kernel was adjusted in the shallow channel to reduce the parameters, ensuring that the overall outline of the image was restored and that the network converged rapidly. Finally, the dual-channel loss function was jointly optimized to enhance the feature-fitting ability in order to obtain the final high-resolution image output. Using the improved algorithm, the network converged more rapidly, the image edge and texture reconstruction effect were obviously improved, and the Peak Signal-to-Noise Ratio (PSNR) and structural similarity were also superior to those of other solutions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Image Processing Techniques · Image Processing Techniques and Applications · Advanced Vision and Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer and Communication Engineering & Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha 410114, China
School of Information Science and Engineering, Fujian University of Technology, Fujian 350118, China
School of Computing Science and Engineering, Vellore Institute of Technology University, Vellore 632014, India
Technical Quality Department, Hunan ZOOMLION Heavy Industry Intelligent Technology Corporation Limited, Changsha 410005, China
School of Hydraulic Engineering & Hunan Provincial Science and Technology Innovation Platform of Key Laboratory of Dongting Lake Aquatic Eco-Environmental Control and Restoration, Changsha University of Science and Technology, Changsha 410114, China
Funding
National Natural Science Foundation of China · the Open Research Fund of Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation · the Changsha Science and Technology Planning
References (46)
Show source-provided references
1. Eigenface-Domain Super-Resolution for Face Recognition10.1109/TIP.2003.811513 ↗
2. A Two-Channel Convolutional Neural Network for Image Super-Resolution10.1016/j.neucom.2017.08.041 ↗
3. A Super-Resolution Reconstruction Algorithm for Surveillance Images10.1016/j.sigpro.2009.09.002 ↗
4. Shi, W.Z., Caballero, J., Ledig, C., Zhuang, X.H., Bai, W.J., Bhatia, K.K., Marvao, A.M.M.D., Dawes, T., O’Regan, D.P., and Rueckert, D. (2013, January 22–26). Cardiac Image Super-Resolution with Global Correspondence using Multi-Atlas Patchmatch. Proceedings of the 2013 Medical image computing and computer-assisted intervention: MICCAI, Nagoya, Japan.10.1007/978-3-642-40760-4_2 ↗
5. Chen, Y.T., Wang, J., Xia, R.L., Zhang, Q., Cao, Z.H., and Yang, K. (2019). The Visual Object Tracking Algorithm Research Based on Adaptive Combination Kernel. J. Ambient Intell. Humaniz. Comput., 1–19.
6. Chen, Y.T., Xiong, J., Xu, W.H., and Zuo, J.W. (2018). A Novel Online Incremental and Decremental Learning Algorithm Based on Variable Support Vector Machine. Clust. Comput., 1–11.10.1007/s10586-018-1772-4 ↗
7. Zhang, J.M., Jin, X.K., Sun, J., Wang, J., and Sangaiah, A.K. (2018). Spatial and Semantic Convolutional Features for Robust Visual Object Tracking. Multimed. Tools Appl., 1–21.10.1007/s11042-018-6562-8 ↗
8. An Asynchronous Clustering and Mobile Data Gathering Schema based on Timer Mechanism in Wireless Sensor Networks
9. Timofte, R., De Smet, V., and Van Gool, L. (2013, January 1–8). Anchored Neighborhood Regression for Fast Example-Based Super-Resolution. Proceedings of the 2013 IEEE International Conference Computer Vision, Sydney, Australia.10.1109/ICCV.2013.241 ↗
10. Timofte, R., De Smet, V., and Van Gool, L. (2014, January 1–5). A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution. Proceedings of the 2014 Asian Conference Computer Vision, Singapore, Singapore.10.1007/978-3-319-16817-3_8 ↗
11. Yang, J.C., Wright, J., Huang, T.S., and Ma, Y. (2008, January 24–26). Image Super-Resolution as Sparse Representation of Raw Image Patches. Proceedings of the 2008 IEEE Conference Computer Vision and Pattern Recognition, Anchorage, AK, USA.
12. Image Super-Resolution Via Sparse Representation10.1109/TIP.2010.2050625 ↗
13. Wang, J., Gao, Y., Liu, W., Sangaiah, A.K., and Kim, H.J. (2019). An Intelligent Data Gathering Schema with Data Fusion Supported for Mobile Sink in WSNs. Int. J. Distrib. Sens. Netw., 15.10.1177/1550147719839581 ↗
14. Chen, Y.T., Xia, R.L., Wang, Z., Zhang, J.M., Yang, K., and Cao, Z.H. (2019). The Visual Saliency Detection Algorithm Research Based on Hierarchical Principle Component Analysis Method. Multimedia Tools Appl., 78.
15. Yang, Y., Lin, Z., and Cohen, S. (2013, January 23–28). Fast image super-resolution based on in-place example regression. Proceedings of the 2013 IEEE Conference on Computer Vision Pattern Recognition, Portland, OR, USA.10.1109/CVPR.2013.141 ↗
16. Multi-Camera Transfer GAN for Person Re-Identification10.1016/j.jvcir.2019.01.029 ↗
17. Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3–6). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the 2012 International Conference Neural Information Processing Systems, Lake Tahoe, NV, USA.
18. Image Super-Resolution using Deep Convolutional Networks10.1109/TPAMI.2015.2439281 ↗
19. Dong, C., Loy, C.C., He, K.M., and Tang, X.O. (2014, January 6–12). Learning a Deep Convolutional Network for Image Super-Resolution. Proceedings of the 2014 International European Conference on Computer Vision, Zurich, Switzerland.10.1007/978-3-319-10593-2_13 ↗
20. End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks10.1109/ACCESS.2019.2903582 ↗
21. Kim, J., Lee, J.K., and Lee, K.M. (2016, January 27–30). Accurate Image Super-Resolution using Very Deep Convolutional Networks. Proceedings of the 2016 IEEE Conference on Computer Vision Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.182 ↗
22. Yang, J.X., Zhao, Y.Q., Chan, J.C.W., and Yi, C. (2016, January 10–15). Hyperspectral Image Classification using Two-Channel Deep Convolutional Neural Network. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium, Beijing, China.10.1109/IGARSS.2016.7730324 ↗
23. Kim, J., Lee, J.K., and Lee, K.M. (2016, January 27–30). Deeply-Recursive Convolutional Network for Image Super-Resolution. Proceedings of the 2016 IEEE Conference on Computer Vision Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.181 ↗
24. Ke, R.M., Li, W., Cui, Z.Y., and Wang, Y.H. (2019, June 04). Two-Stream Multi-Channel Convolutional Neural Network (TM-CNN) for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact. Available online: https://arxiv.org/abs/1903.01678.
25. Lim, B., Son, S., Kim, H., Nah, S., and Lee, K.M. (2017, January 21–26). Enhanced Deep Residual Networks for Single Image Super-Resolution. Proceedings of the 2017 IEEE Conference on Computer Vision Pattern Recognition, Workshops, Honolulu, HI, USA.10.1109/CVPRW.2017.151 ↗
26. Tai, Y., Yang, J., Liu, X.M., and Xu, C.Y. (2017, January 22–29). MemNet: A Persistent Memory Network for Image Restoration. Proceedings of the 2017 IEEE International Conference Computer Vision, Venice, Italy.10.1109/ICCV.2017.486 ↗
27. Security in Hardware Assisted Virtualization for Cloud Computing—State of the Art Issues and Challenges10.1016/j.comnet.2019.01.013 ↗
28. Region-Based Compressive Networked Storage with Lazy Encoding10.1109/TPDS.2018.2883550 ↗
29. Unified Blind Quality Assessment of Compressed Natural, Graphic, and Screen Content Images10.1109/TIP.2017.2735192 ↗
30. Using Free Energy Principle for Blind Image Quality Assessment10.1109/TMM.2014.2373812 ↗
31. He, K.M., Zhang, X.Y., Ren, S.Q., and Sun, J. (2016, January 27–30). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE International Conference on Computer Vision Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
32. Nair, V., and Hinton, G.E. (2010, January 21–24). Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the 2010 International Conference on Machine Learning, Haifa, Israel.
33. He, K.M., Zhang, X.Y., Ren, S.Q., and Sun, J. (2015, January 7–13). Delving Deep into Rectifiers: Surpassing Human-Level Performance on Imagenet Classification. Proceedings of the 2015 IEEE International Conference on Computer Vision, Santiago, Chile.10.1109/ICCV.2015.123 ↗
34. Sun, J., Xu, Z.B., and Shum, H.Y. (2008, January 24–26). Image Super-Resolution using Gradient Profile Prior. Proceedings of the 2008 IEEE International Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA.
35. Nah, S., Kim, T.H., and Lee, K.M. (2017, January 21–26). Deep Multi-Scale Convolutional Neural Network for Dynamic Scene Deblurring. Proceedings of the 2016 IEEE Conference on Computer Vision Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.35 ↗
36. Zeiler, M.D., and Fergus, R. (2014, January 6–12). Visualizing and Understanding Convolutional Networks. Proceedings of the 2014 European Conference on Computer Vision, Zurich, Switzerland.10.1007/978-3-319-10590-1_53 ↗
37. Goodfellow, I., Pouget-Adadie, J., Mirza, M., Xu, B., Farley, D.W., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8–13). Generative Adversarial Nets. Proceedings of the 2014 Advances in Neural Information Processing Systems, Montreal, QC, Canada.
38. Timofte, R., Agustsson, E., Gool, L.V., Yang, M.H., Zhang, L., Lim, B., Son, S., Kim, H., Nah, S., and Lee, K.M. (2017, January 21–26). Ntire 2017 challenge on single image super-resolution: Methods and results. Proceedings of the 2017 IEEE Conference on Computer Vision Pattern Recognition, Workshops, Honolulu, HI, USA.10.1109/CVPRW.2017.150 ↗
39. Bevilacqua, M., Roumy, A., Guillemot, C., and Alberi-Morel, M.L. (2018, January 3–6). Low-Complexity Single-Image Super-Resolution Based on Nonnegative Neighbor Embedding. Proceedings of the 2012 British Machine Vision Conference, Northumbria University, Newcastle, UK.
40. Zeyde, R., Elad, M., and Protter, M. (2010, January 24–30). On Single Image Scale-up using Sparse-Representations. Proceedings of the 2010 International Conference Curves and Surfaces, Avignon, France.
41. Unmanned Aerial Vehicles using Machine Learning for Autonomous Flight; State-of-The-Art10.1080/01691864.2019.1586760 ↗
42. Gao, G.W., Zhu, D., Yang, M., Lu, H.M., Yang, W.K., and Gao, H. (2018). Face Image Super-Resolution with Pose via Nuclear Norm Regularized Structural Orthogonal Procrustes Regression. Neural Comput. Appl., 1–11.10.1007/s00521-018-3826-1 ↗
43. Single-Image Super-Resolution Algorithm Based on Structural Self-Similarity and Deformation Block Features10.1109/ACCESS.2019.2911892 ↗
44. Hong, P.L., and Zhang, G.Q. (2019). A Review of Super-Resolution Imaging through Optical High-Order Interference. Appl. Sci., 9.10.3390/app9061166 ↗
45. Pan, C., Lu, M.Y., Xu, B., and Gao, H.L. (2019). An Improved CNN Model for Within-Project Software Defect Prediction. Appl. Sci., 8.10.3390/app9102138 ↗
46. Mobile Marketing Recommendation method Based on User Location Feedback10.1186/s13673-019-0177-6 ↗