Denoising of Volumetric MR Image Using Low-Rank Approximation on Tensor SVD Framework

摘要

In this paper, we focus on denoising of additively corrupted volumetric magnetic resonance (MR) images for improved clinical diagnosis and further processing. We have considered three dimensional MR images as third-order tensors. MR image denoising is solved as a low-rank tensor approximation problem, where the non-local similarity and correlation existing in volumetric MR images are exploited. The corrupted images are divided into 3D patches and similar patches form a group matrix. The group matrices exhibit low-rank property and is decomposed with tensor singular value decomposition (t-SVD) technique, and reweighted iterative thresholding is performed on core coefficients for removing the noise. The proposed method is compared with the state-of-the-art methods and has shown improved performance.