期刊文献+

非高斯噪声环境下基于扩展基追踪模型的信号去噪

Signal Denoising Based on Extended Basis Pursuit Model under Non-Gaussian Noise Circumstance
下载PDF
导出
摘要 经典基追踪模型中所考虑的噪声是加性的高斯白噪声,而实际应用中噪声的形式是多种多样的。因此,经典基追踪模型不能满足处理非高斯噪声环境下的信号去噪问题。基于不同的稀疏性度量函数和不同的拟合误差项形式,对经典基追踪模型进行了扩展,提出了新的基追踪扩展模型,并分析了扩展模型的统计意义。针对其中一类扩展模型,给出了其求解算法。在脉冲噪声环境下的信号去噪实验结果验证了该模型具有比经典基追踪模型更显著的去噪效果。 Traditional basis pursuit model is considered in the presence of additive Gaussian white noise.Eventually,there may be many kinds of noise circumstances in real application.Obviously,traditional basis pursuit model is not enough for non-Gaussian noise circumstances.Based on variable sparseness measure functions and error penalty functions,new extended Basis pursuit model is proposed.Statistical significance of extended basis pursuit model is analyzed,algorithm of a kind of extended basis pursuit model is also brought forward.Some typical signal denosing experiments results demonstrate that the extended Basis Pursuit model can provide better de-noising results than traditional Basis Pursuit model wherein the noise is impulse noise.
出处 《现代电子技术》 2008年第11期1-3,6,共4页 Modern Electronics Technique
基金 国家自然科学基金资助项目(60572136)
关键词 基追踪 扩展模型 字典 正则化参数 脉冲噪声 去噪 basis pursuit extended model dictionary regularization parameter impulse noise denoising
  • 相关文献

参考文献8

  • 1Chen S,Donoho D L, Saunders M A. Atomic Decomposition by Basis Pursuit[J]. SIAM Sci. Comp. ,1999,20(1) :33 - 61.
  • 2Donoho D L,Michael Elad. Optimally Sparse Representation in General Dictionaries via l1 Minimization[J]. PNAS,2003, 100(5) :2 197- 2 202.
  • 3Donoho D L. For Most Large Underdetermined Systems of Linear Equations, the minimal l1- norm near - solution approximates the sparsest near - solution[EB/OL]. Technical Report, 2004 - 11, Department of Statistics, Stanford University, 2004, http://www.-stat.stanford. edu/-donoho/ reports. html.
  • 4Brian D Jeffs, Metin Gunsay. Restoration of Blurred Star Field Images by Maximally Sparse Optimization[J]. IEEE Transactiona on Image Processing, 1993,2 (2) : 202 - 211
  • 5Wang Xiongliang,Wang Zhengming, Wang Chunling. Target Feature- Enhanced of SAR Image Based on Regularization of lk Norm[C]. International Conference on Space Information Technology (ICSIT/2005) ,China,Proc. of SPIE,2005,
  • 6周宏潮.基于稀疏参数模型及参数先验的图像分辨率增强方法研究[D].长沙:国防科技大学,2005.
  • 7Donoho D L. Denoising by Soft Thresholding[J].IEEE Transactions on Information Theory, 1995(41) : 613 - 627
  • 8MakeSignal. m[EB/OL], http://www- stat. stanford.edu/- wavelab.

共引文献3

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