期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

不同采样模式的固体DQ-SQ实验的压缩感知重建比较 被引量：3

Comparison of Different Sampling Schemes in Compressed Sensing Reconstruction for DQ-SQ experiments
下载PDF
导出
摘要 为了提高固体二维双量子-单量子(DQ-SQ)谱的采集速度,根据DQ-SQ谱的自稀疏性,该文使用了一种基于压缩感知技术的重建算法.其优化的能量函数是有限差分约束的l1范数,并使用不同的权重对水平和竖直方向的有限差分项进行约束.该文分别对伪随机采样、全随机采样和e指数采样等采样模式进行了比较,发现伪随机采样表现出最佳的重建结果.进一步研究发现伪随机的极限形式,即t1截尾(t1-cutoff)采样模式效果最佳. To increase the speed of acquisition of two-dimensional solid-state DQ-SQ spectrum, a compressed sensing algorithm which makes use of the self-sparsity of the spectrum to construct under-sampled data. The energy function used in optimization is l1 norm together with the finite difference term. In the finite different term, we used different weights for the horizontal and vertical finite differences. Different sampling schemes were compared and pseudo-random sampling combined with compressed sensing reconstruction was found to yield the best results. Furthermore, we found that the extreme case of pseudo-random sampling, that is, t1-cutoff sampling may be the best choice.
作者 郑慧 韩明月 胡炳文 杨光
机构地区 华东师范大学物理系、上海市磁共振重点实验室
出处 《波谱学杂志》 CAS CSCD 北大核心 2014年第4期535-547,共13页 Chinese Journal of Magnetic Resonance
基金 上海市科委资助项目(08DZ1900700)
关键词 固体核磁共振(solid-state NMR) 压缩感知 采样模式 DQ-SQ 伪随机 DQ-SQ solid-state NMR compressed sensing sampling scheme DQ-SQ pseudo-random
分类号 O482.53 [理学—固体物理]
  • 相关文献

参考文献25

  • 1Jaravine V I I, Orekhov V Y. Removal of a time barrier for high-resolution multidimensional NMR spectroscopy[J]. Nature Methods, 2006, 3(8): 605-607.
  • 2Bruschweiler R. Theory of covariance nuclear magnetic resonance spectroscopy[J]. I Chem Pb_ys, 2004, 121 ( 1 ): 409 - 414.
  • 3Hu B W, Zhou P, Noda I, et al. An NMR approach applicable to biomolecular structme characterization[J]. Anal Chem, 2005, 77(23): 7 534-7 538.
  • 4Barua J C J, Lane E D, Mayger M R, et al. Exponential sampling, an alternative method for sampling in two-dimemsional NMR experiments[J]. J Magn Reson, 1987, 73(1): 69-77.
  • 5Jeffrey C, Hoch A S S. Maximum entropy reconstruction, spectrum analysis and deconvolution in multidimensional nuclear magnetic resonance[J]. Method Enzymol, 2002, 338: 159- 178.
  • 6Kazimierczuk K, Orekhov V Y. Accelerated NMR spectroscopy by using compressed sensing[J]. Angew Chem Int Ed, 2011, 50(24): 5 556-5 559.
  • 7Coggins B E, Zhou P. High resolution 4-D spectroscopy with sparse concentric shell sampling and FFT-CLEAN[J]. J Biomol NMR, 2008, 42(4): 225 -239.
  • 8Kazimierczuk K, Kozminski W, Zhukov I. Two-dimensional Fourier transform of arbitrarily sampled NMR data sets[J]. J Magn Reson, 2006, 179(2): 323 -328.
  • 9Kazimierezuk K, Zawadzka A, Kozminski W, et al. Random sampling of evolution time space and fourier transform processing[J]. J Biomol NMR, 2006, 36(3): 157 - 168.
  • 10Emmanuel J C, Justin R, Member L, et al. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE T Inf Theory, 2006, 52(2): 489-509.

二级参考文献61

  • 1Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
  • 2Candes E, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 2006, 52(2): 489-509.
  • 3Candes E. Compressive sampling. In: Proceedings of International Congress of Mathematicians. Madrid, Spain: European Mathematical Society Publishing House, 2006. 1433-1452.
  • 4Baraniuk R G. Compressive sensing. IEEE Signal Processing Magazine, 2007, 24(4): 118-121.
  • 5Olshausen B A, Field D J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 1996, 381(6583): 607-609.
  • 6Mallat S. A Wavelet Tour of Signal Processing. San Diego: Academic Press, 1996.
  • 7Candes E, Donoho D L. Curvelets - A Surprisingly Effective Nonadaptive Representation for Objects with Edges, Technical Report 1999-28, Department of Statistics, Stanford University, USA, 1999.
  • 8Aharon M, Elad M, Bruckstein A M. The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representations. IEEE Transactions on Image Processing, 2006, 54(11): 4311-4322.
  • 9Rauhut H, Schnass K, Vandergheynst P. Compressed sensing and redundant dictionaries. IEEE Transactions on Information Theory, 2008, 54(5): 2210-2219.
  • 10Candes E, Romberg J. Sparsity and incoherence in compressive sampling. Inverse Problems, 2007, 23(3): 969-985.

共引文献204

同被引文献40

  • 1Ernst R R, Bodenhausen G, Wokaun A. Principles of Nuclear Magnetic Resonance in One and Two Dimensions[M]. Oxford: Oxford University Press, 1990.
  • 2Massiot D, Fayon F, Capron M, et al. Modelling one and two dimensional solid-state NMR spectra[J]. Magn Reson Chem, 2002, 40(1): 70-76.
  • 3Kazimierczuk K, Orekhov V Y. A comparison of convex and non-convex compressed sensing applied to multidimensional NMR[J]. J Magn Reson, 2012, 223:1 -10.
  • 4Bruschweiler R. Theory of covariance nuclear magnetic resonance spectroscopy[J]. J Chem Phys, 2004, 121 (1): 409-414.
  • 5Daniell G J, Hore P J. Maximum entropy and NMR-A new approach[J]. J Magn Reson, 1989, 84(3): 515-536.
  • 6Coggins B E, Zhou P. High resolution 4-D spectroscopy with sparse concentric shell sampling and FFT-CLEAN[J]. J Biomol NMR, 2008, 42(4): 225-239.
  • 7Lustig M, Donoho D, Pauly J M. Sparse MRI: The application of compressed sensing for rapid MR imaging[J]. Magn Reson Med, 2007, 58(6): 1 182- 1 195.
  • 8Baraniuk R G. Compressive sensing[J]. IEEE Signal Proc Mag, 2007, 24(4): 118-124.
  • 9Foucart S, Rauhut H. A Mathematical Introduction to Compressive Sensing[M]. Berlin: Springer, 2013.
  • 10Donoho D L. Compressed sensing[J]. IEEE T Inform Theory, 2006, 152(4): 1 289- 1 306.

引证文献3

二级引证文献6

波谱学杂志
2014年 第4期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部