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Study on the Application of Improved Deep Convolutional Neural Network Algorithm in Broken Information Recovery

   | 26 févr. 2024
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Matsuo, Y., Kimura, T., & Nishimatsu, K. (2021). Deepsip: a system for predicting service impact of network failure by temporal multimodal cnn. IEICE Transactions on Communications(10). Search in Google Scholar

Ding, G., Liu, Y., Zhang, R., & Xin, H. L. (2019). A joint deep learning model to recover information and reduce artifacts in missing-wedge sinograms for electron tomography and beyond. Scientific Reports, 9(1), 1-13. Search in Google Scholar

Choi, Y. R., & Kil, R. M. (2021). Face video retrieval based on the deep cnn with rbf loss. IEEE Transactions on Image Processing, 30, 1015-1029. Search in Google Scholar

Bhaskaran, S. K., Sreejith, C., & Rafeeque, P. C. (2018). Neural networks and conditional random fields based approach for effective question processing. Procedia Computer Science. Search in Google Scholar

Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2019). A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review(1-87). Search in Google Scholar

Yan, B., & Sinitsyn, N. A. (2020). Recovery of damaged information and the out-of-time-ordered correlators. Physical Review Letters. Search in Google Scholar

Li, F., Bai, H., & Zhao, Y. (2019). Detail-preserving image super-resolution via recursively dilated residual network. Neurocomputing, 358(SEP.17), 285-293. Search in Google Scholar

Lei, J., & Lei, Z. (2011). The Chaotic Cipher Based on CNNs and Its Application in Network. Information Processing and Trusted Computing. IEEE. Search in Google Scholar

Jinjiang, L., Guihui, L., & Hui, F. (2018). Image dehazing using residual-based deep cnn. IEEE Access, PP, 1-1. Search in Google Scholar

Eilertsen, G., Kronander, J., Denes, G., Mantiuk, R. K., & Unger, J. (2017). Hdr image reconstruction from a single exposure using deep cnns. Acm Transactions on Graphics, 36(6). Search in Google Scholar

Maltezos, E., Doulamis, N., Doulamis, A., & Ioannidis, C. (2017). Deep convolutional neural networks for building extraction from orthoimages and dense image matching point clouds. Journal of Applied Remote Sensing, 11(4), 042620-1-042620-22. Search in Google Scholar

Liu, YisenZhou, SongbinHan, WeiLi, ChangLiu, WeixinQiu, Zefan. (2019). Hyperspectral classification using deep fusion spectral-spatial features. Journal of Applied Remote Sensing, 13(3). Search in Google Scholar

Passah, A., Amitab, K., & Kandar, D. (2021). Sar image despeckling using deep cnn. IET Image Processing, 15. Search in Google Scholar

Jiao, L., Wu, H., Wang, H., & Bie, R. (2018). Text recovery via deep cnn-bilstm recognition and bayesian inference. IEEE Access, 1-1. Search in Google Scholar

eISSN:
2444-8656
Langue:
Anglais
Périodicité:
Volume Open
Sujets de la revue:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics