# Low-Rank Tensor Completion via Novel Sparsity-Inducing Regularizers

This paper proposes a framework to generate sparsity-inducing regularizers with closed-form proximity operators, which avoids iterations. 

For more information, please see our paper "Low-Rank Tensor Completion via Novel Sparsity-Inducing Regularizers".

If you use our codes, please cite the following papers:

1. Z. -Y. Wang, H. C. So and A. M. Zoubir, "Low-Rank Tensor Completion via Novel Sparsity-Inducing Regularizers," in IEEE Transactions on Signal Processing, vol. 72, pp. 3519-3534, Aug. 2024.
2. Z.-Y. Wang, H. C. So and A. M. Zoubir, ``Robust low-rank matrix recovery via hybrid ordinary-Welsch function,'' IEEE Trans. Signal Process., vol. 71, pp. 2548-2563, Jul. 2023.
3. Z. -Y. Wang, H. C. So and A. M. Zoubir, "Robust low-rank matrix completion via sparsity-inducing regularizer," Signal Process., vol. 226, pp. 109666, 2024.

If you have any questions, please email us. Thanks.
