Adaptive deep learning network for image reconstruction of compressed sensing
Author:
Funder
Natural Science Foundation of Tianjin Municipality
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-023-02879-3.pdf
Reference45 articles.
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3. Liutkus, A., Martina, D., Popoff, S., Chardon, G., Katz, O., Lerosey, G., Gigan, S., Daudet, L., Carron, I.: Imaging with nature: compressive imaging using a multiply scattering medium. Sci. Rep. 4(4), 5552 (2014). https://doi.org/10.1038/srep05552
4. Sekar, K., Devi, K.S., Srinivasan, P.: Compressed tensor completion: a robust technique for fast and efficient data reconstruction in wireless sensor networks. IEEE Sens. J. 22(11), 10794–10807 (2022). https://doi.org/10.1109/JSEN.2022.3169226
5. Roohi, S.F., Zonoobi, D., Kassim, A.A., Jaremko, J.L.: Multi-dimensional low rank plus sparse decomposition for reconstruction of under-sampled dynamic mri. Pattern Recognit. 63, 667–679 (2016). https://doi.org/10.1016/j.patcog.2016.09.040
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