MRI denoising via sparse tensors with reweighted regularization.

Highlights • We present a sparse tensor denoising model for magnetic resonance images. • The convergence of proposed scheme is proved. • The optional solution is proved. • Numerical algorithm is developed. • Comparison with experimental results is covered. Abstract In recent years, image denoising based on sparse tensors has been one promising technique for denoising magnetic resonance images or video processing. This paper aims at developing a new sparse tensor model based on reweighted regularization of factor matrices for magnetic resonance images denoising. An improved Split-Bregman scheme is proposed which is simple in implementation and efficient in computation. Additionally, the convergence of proposed scheme is proved. Experiments show that the proposed algorithm is efficient, and the denoising results are better than the state-of-the-art image denoising methods. The average computational time of our method is slightly longer than the others under the same iteration, except LPGPCA and model in Ruru and Zhixun (2018) [22]. [ABSTRACT FROM AUTHOR]