有限投影CT图像重构的1- 1范数和1- 0范数正则化算法。

IF 3.3 Q2 ENGINEERING, BIOMEDICAL
International Journal of Biomedical Imaging Pub Date : 2020-08-28 eCollection Date: 2020-01-01 DOI:10.1155/2020/8873865
Xiezhang Li, Guocan Feng, Jiehua Zhu
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引用次数: 2

摘要

1.1范数正则化在计算机断层扫描图像重建中受到了广泛的关注。图像梯度的0范数提供了图像梯度稀疏度的度量。本文提出了一种新的l - 1范数和l - 0范数组合正则化模型,用于计算机断层扫描中有限投影数据的图像重建。在代数框架下,提出了一种采用硬阈值法的非单调交替方向算法来有效解决优化问题的算法。数值实验表明,通过引入0范数正则化,该算法有了很大的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

An Algorithm of <i>l</i> <sub>1</sub>-Norm and <i>l</i> <sub>0</sub>-Norm Regularization Algorithm for CT Image Reconstruction from Limited Projection.

An Algorithm of <i>l</i> <sub>1</sub>-Norm and <i>l</i> <sub>0</sub>-Norm Regularization Algorithm for CT Image Reconstruction from Limited Projection.

An Algorithm of <i>l</i> <sub>1</sub>-Norm and <i>l</i> <sub>0</sub>-Norm Regularization Algorithm for CT Image Reconstruction from Limited Projection.

An Algorithm of l 1-Norm and l 0-Norm Regularization Algorithm for CT Image Reconstruction from Limited Projection.

The l 1-norm regularization has attracted attention for image reconstruction in computed tomography. The l 0-norm of the gradients of an image provides a measure of the sparsity of gradients of the image. In this paper, we present a new combined l 1-norm and l 0-norm regularization model for image reconstruction from limited projection data in computed tomography. We also propose an algorithm in the algebraic framework to solve the optimization effectively using the nonmonotone alternating direction algorithm with hard thresholding method. Numerical experiments indicate that this new algorithm makes much improvement by involving l 0-norm regularization.

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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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