A New De-noising Method for Infrared Spectrum

Selecting the most appropriate algorithms for reducing the noise component in infrared spectrum is very necessary, since the infrared signal is often corrupted by noise. To solve this problem, a novel de-noising method based on the null space pursuit (NSP) is proposed in this paper. The NSP is the adaptive operator-based signal separation approach, which can decompose the signal into sub-band components and the residue according to their characteristics. We consider the residue as noise, because it basically dose not contain any useful information. Then, the sub-band components are used to reconstructing the ideal signal. Experimental results show that the proposed de-noising method is effective in suppressing noise while protecting signal characteristics.

[1]  Wen-Liang Hwang,et al.  Estimation of Instantaneous Frequency Parameters of the Operator-Based Signal Separation Method , 2009, Adv. Data Sci. Adapt. Anal..

[2]  Wen-Liang Hwang,et al.  Operator based multicomponent AM-FM signal separation approach , 2011, 2011 IEEE International Workshop on Machine Learning for Signal Processing.

[3]  S. Peng,et al.  Image super-resolution based on Null Space Pursuit , 2010, 2010 3rd International Congress on Image and Signal Processing.

[4]  Jing-Yu Yang,et al.  Weighted features for infrared vehicle verification based on Gabor filters , 2004, ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004..

[5]  A. Barth Infrared spectroscopy of proteins. , 2007, Biochimica et biophysica acta.

[6]  Zukang Lu,et al.  Infrared Spectrum Visualizing Human Acupoints and Meridian-like Structure , 2006, 2006 International Symposium on Biophotonics, Nanophotonics and Metamaterials.

[7]  L. T. Ho Infrared absorption spectrum of magnesium double donors in silicon , 2005, 2005 Joint 30th International Conference on Infrared and Millimeter Waves and 13th International Conference on Terahertz Electronics.

[8]  Wen-Liang Hwang,et al.  Adaptive Signal Decomposition Based on Local Narrow Band Signals , 2008, IEEE Transactions on Signal Processing.

[9]  Dan Peng,et al.  A Wavelet Component Selection Method for Multivariate Calibration of Near-Infrared Spectra Based on Information Entropy Theory , 2010, 2010 International Conference on Biomedical Engineering and Computer Science.

[10]  Wen-Liang Hwang,et al.  Null Space Pursuit: An Operator-based Approach to Adaptive Signal Separation , 2010, IEEE Transactions on Signal Processing.

[11]  Jingchang Pan,et al.  Astronomical spectra denoising based on simplified SURE-LET wavelet thresholding , 2008, 2008 International Conference on Information and Automation.