A nonlocal enhanced Low-Rank tensor approximation framework for 3D Magnetic Resonance image denoising. (February 2022) - Wang, Li; Xiao, Di; Hou, Wen S.; Wu, Xiao Y.; Jiang, Bin; Chen, Lin
Highlights: The Tucker-rank was estimated through low-rank tensor approximation with consideration of image nonlocal similarity. An enhanced low-rank tensor regularization using a Logarithmic-Sum method which adaptively rescale singular value with different weights has been developed for the tensor rank minimization problem. Abstract: Magnetic Resonance Imaging (MRI) has become an increasingly essential tool in clinical detection and diagnosis for its ability to disclose the distinctive information of human anatomic structures in vivo. However, the image intensity in magnitude MRI images is frequently corrupted by Rician noise which is inherent to the acquisition process. This study aimed to propose a denoising model for 3D MR image restoration based on low-rank tensor approximation with a Logarithmic-Sum regularization framework. Nonlocal similarity of images was exploited with grouping and block matching techniques, and tensor decomposition approaches were utilized taking consideration of multi-dimensional structural dependence conservation. The low-rank tensor approximation problem was regulated by introducing a Logarithmic-Sum trace norm, which thresholded the singular value spectrum adaptively with different thresholds. Noise removal was processed through the low-rank approximation, and the denoised tensor blocks were aggregated to restore the clear images. Numerical experiments were conducted under comprehensive noise conditions for 3D MR volumetric datasets. Through denoising comparison, the results demonstrated that the proposed algorithm achieves state-of-the-art performance for Rician noise removal with excellent detail preservation..