Paper Info
Title
Analysis ${{L_{{1/2}}}}$ Regularization: Iterative Half Thresholding Algorithm for CS-MRI.
Abstract
Recently, the L-1/2 regularization has shown its great potential to eliminate the bias problems caused by the convex L-1 regularization in many compressive sensing (CS) tasks. CS-based magnetic resonance imaging (CS-MRI) aims at reconstructing a high-resolution image from under-sampled k-space data, which can shorten the imaging time efficiently. Theoretically, the L-1/2 regularization-based CS-MRI will reconstruct the MR images with higher quality to investigate and study the potential and feasibility of the L-1/2 regularization for the CS-MRI problem. In this paper, we employ the nonconvex L-1/2-norm to exploit the sparsity of the MR images under the tight frame. Then, two novel iterative half thresholding algorithms (IHTAs) for the analysis of the L-1/2 regularization are introduced to solve the nonconvex optimization problem, namely, smoothing-IHTA and projected-IHTA. To evaluate the performance of the L-1/2 regularization, we conduct our experiments on the real-world MR data using three different popular sampling masks. All experimental results demonstrate that the L-1/2 regularization can improve the L-1 regularization significantly and show the potential and feasibility for future practical applications.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2923171
IEEE ACCESS
Keywords
Field
DocType
L-1/2 regularization,compressive sensing,analysis model,iterative half thresholding algorithm,tight frame,smoothing,magnetic resonance imaging
Thresholding algorithm,Computer science,Algorithm,Regularization (mathematics),Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Lianjun Yuan100.34
Yunyi Li244.46
Fei Dai382.54
Yan Long4396.41
Xiefeng Cheng576.74
Guan Gui6641102.53