Parametric spectral signal restoration via maximum entropy constraint and its application

In this paper, we will propose a new framework which can estimate the desired signal and the instrument response function (IRF) simultaneously from the degraded spectral signal. Firstly, the spectral signal is considered as a distribution, thus, new entropy (called differential-entropy, DE) is defined to measure the distribution with a uniform distribution, which allows negative value existing. Moreover, the IRF is parametrically modeled as a Lorentzian function. Comparative results manifest that the proposed method outperforms the conventional methods on peak narrowing and noise suppression. The deconvolution IR spectrum is more convenient for extracting the spectral feature and interpreting the unknown chemical mixtures.

[1]  Ling-Jian Meng,et al.  An inter-comparison of three spectral-deconvolution algorithms for gamma-ray spectroscopy , 1999 .

[2]  D. Ramsden,et al.  An inter-comparison of three spectral-deconvolution algorithms for gamma-ray spectroscopy , 1999, 1999 IEEE Nuclear Science Symposium. Conference Record. 1999 Nuclear Science Symposium and Medical Imaging Conference (Cat. No.99CH37019).

[3]  Hai Liu,et al.  Spectral Deconvolution and Feature Extraction With Robust Adaptive Tikhonov Regularization , 2013, IEEE Transactions on Instrumentation and Measurement.

[4]  Jianwen Sun,et al.  Adaptive total variation-based spectral deconvolution with the split Bregman method. , 2014, Applied optics.

[5]  J. Shore Minimum cross-entropy spectral analysis , 1981 .

[6]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[7]  Tianxu Zhang,et al.  Multi-order blind deconvolution algorithm with adaptive Tikhonov regularization for infrared spectroscopic data , 2015 .

[8]  Ziqiang Hu,et al.  High-order cumulant-based blind deconvolution of Raman spectra. , 2005, Applied optics.

[9]  P. Davies,et al.  Local Extremes, Runs, Strings and Multiresolution , 2001 .

[10]  Émilie Chouzenoux,et al.  Efficient Maximum Entropy Reconstruction of Nuclear Magnetic Resonance T1-T2 Spectra , 2010, IEEE Transactions on Signal Processing.

[11]  Hai Liu,et al.  A MAP-based algorithm for spectroscopic semi-blind deconvolution. , 2012, The Analyst.

[12]  VÍCTOR A. LÓRENZ-FONFRÍA,et al.  The Role and Selection of the Filter Function in Fourier Self-Deconvolution Revisited , 2009, Applied spectroscopy.

[13]  Chein-I Chang,et al.  Automatic spectral target recognition in hyperspectral imagery , 2003 .

[14]  Richard G. Baraniuk,et al.  ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems , 2004, IEEE Transactions on Signal Processing.

[15]  F. Meer The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery , 2006 .

[16]  Xiangyan Liu,et al.  An Infrared Scanning and Tracking System for Detecting Mid-Wave Infrared Spectral Characteristics of Moving Targets , 2014, Applied spectroscopy.

[17]  Samantha Boyd,et al.  Raman spectroscopy of blood samples for forensic applications. , 2011, Forensic science international.

[18]  A. El-Jaroudi,et al.  Evolutionary spectrum estimation by positivity constrained deconvolution , 1998, Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis (Cat. No.98TH8380).