1. Ahmed, A., Demanet, L.: Leveraging diversity and sparsity in blind deconvolution. IEEE Trans. Inf. Theory 64(6), 3975–4000 (2018)
2. Asim, M., Shamshad, F., Ahmed, A.: Blind image deconvolution using deep generative priors. arXiv preprint:1802.04073 (2018)
3. Bora, A., Jalal, A., Price, E., Dimakis, A.G.: Compressed sensing using generative models. In: Proceedings of the 34th International Conference on Machine Learning (ICML), vol. 70, pp. 537–546 (2017).
http://JMLR.org
4. Borgerding, M., Schniter, P., Rangan, S.: AMP-inspired deep networks for sparse linear inverse problems. IEEE Trans. Signal Process. 65(16), 4293–4308 (2017)
5. Flinth, A.: Sparse blind deconvolution and demixing through ℓ
1,2-minimization. Adv. Comput. Math. 44(1), 1–21 (2018)