Nonlinear diffusion filtering for peak-preserving smoothing of a spectrum signal

被引:22
|
作者
Li, Yuanlu [1 ,2 ]
Ding, Yaqing [1 ]
Li, Tiao [1 ,2 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Sch Informat & Control, B DAT, Nanjing 210044, Jiangsu, Peoples R China
[2] Nanjing Univ Informat Sci & Technol, Jiangsu Collaborat Innovat Ctr Atmospher Environm, Nanjing 210044, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Spectra; Nonlinear diffusion; Peak-preserving smoothing; Regularization method; Wavelet method; Savitzky-Golay method; CONTINUOUS WAVELET TRANSFORM; IMAGE NOISE REMOVAL; ANISOTROPIC DIFFUSION; DERIVATIVE SPECTROMETRY; RESOLUTION ENHANCEMENT; POLYNOMIAL FILTER; INFRARED-SPECTRA; EDGE-DETECTION; DIFFERENTIATION; QUANTIFICATION;
D O I
10.1016/j.chemolab.2016.06.007
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
How to reduce the noise while preserving the peak is a challenging task in analytical techniques. In this paper, the nonlinear diffusion was proposed as a general method to accomplish peak-preserving smoothing. The implement of the nonlinear diffusion is simple. Taking the noisy signal as the initial condition of a nonlinear diffusion equation, the solution is a smoothed signal, and signal becomes increasingly smooth as iteration number increases. Details of the nonlinear diffusion filtering and its implementation were given clearly. Some simulated signals and an NMR spectrum has been used to verify the proposed method and compare the performance of other methods such as regularization method, Savitzky-Golay method and wavelet method. Results indicated that the nonlinear diffusion is an excellent smoothing method, it can reduce the noise while preserve the peak shape. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:157 / 165
页数:9
相关论文
共 50 条
  • [41] Nonlinear diffusion filtering on extended neighborhood
    Barash, D
    APPLIED NUMERICAL MATHEMATICS, 2005, 52 (01) : 1 - 11
  • [42] Robust diffusion approximation for nonlinear filtering
    J Math Syst Estim Control, 1 (139-142):
  • [43] Gaussian processes of nonlinear diffusion filtering
    Girdziusas, R
    Laaksonen, J
    Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vols 1-5, 2005, : 1012 - 1017
  • [44] Segment Graph Based Image Filtering: Fast Structure-Preserving Smoothing
    Zhang, Feihu
    Dai, Longquan
    Xiang, Shiming
    Zhang, Xiaopeng
    2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2015, : 361 - 369
  • [45] Tree Filtering: Efficient Structure-Preserving Smoothing With a Minimum Spanning Tree
    Bao, Linchao
    Song, Yibing
    Yang, Qingxiong
    Yuan, Hao
    Wang, Gang
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2014, 23 (02) : 555 - 569
  • [46] Time-fractional diffusion equation for signal smoothing
    Li, Yuanlu
    Liu, Fawang
    Turner, Ian W.
    Li, Tao
    APPLIED MATHEMATICS AND COMPUTATION, 2018, 326 : 108 - 116
  • [47] Dual Kalman filtering methods for nonlinear prediction, smoothing, and estimation
    Wan, EA
    Nelson, AT
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 9: PROCEEDINGS OF THE 1996 CONFERENCE, 1997, 9 : 793 - 799
  • [48] A nonlinear iterative filtering-smoothing algorithm for GPS positioning
    Cao, Yi
    Mao, Xu-Chu
    Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University, 2009, 43 (07): : 1108 - 1112
  • [49] EXACT SEQUENTIAL FILTERING, SMOOTHING AND PREDICTION FOR NONLINEAR-SYSTEMS
    KALABA, R
    TESFATSION, L
    NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS, 1988, 12 (06) : 599 - 615
  • [50] Finite-Time Peak-To-Peak Filtering for Nonlinear Singular System
    Song, Jiasheng
    Chang, Xiao-Heng
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2022, 69 (11) : 4369 - 4373