A De-Noising Algorithm Dealing with Spectral Signal in Color Measurement Based on Wavelet Transform

Abstract:

Spectral measurement is a common method for color measurement. Spectral data of color samples gathered by spectrometers contains a series of noises. The traditional de-noising methods have their limitation in dealing with such signal. In this paper, a de-noising method is proposed for spectral signal based on Wavelet Transform. Compared the performance of our de-nosing method with the traditional method, the results show that our method a better effect.

• Periodical: Applied Mechanics and Materials (Volumes 303-306)
• Pages: 1043-1047
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.303-306.1043
• Online since: February 2013
• Authors: Hai Long Li, Jun Xu, Wen Jia, Sheng Zhu, Sha Li, Meng Meng Wu, Bo Yi
• Keywords: Color Measurement, Colorimeter, De-Noising Algorithm, Spectral Analysis, Threshold, Wavelet Transform (WT)

References

[1] Crouse M.S.; Nowak, R.D.; Baraniuk R.G. Wavelet-based statistical signal processing using hidden Markov models, in Signal Processing, IEEE Transactions on Volume: 46 , pp.886-902.

DOI: 10.1109/78.668544

[2] Ting Yang; Jie Wan; Bin Xu Study on de-noising of near infrared spectrum for wood based on wavelet transform modulus maximum, in Image and Signal Processing (CISP), 2010 3rd International Congress on Volume: 7 p.3242 – 3246.

DOI: 10.1109/cisp.2010.5647997

[3] R.R. Coifman and D.L. Donoho, Translation-invariant de-noising, In Wavelates and statistics, Springer lecture Notes in Statistics 103. New York; Springer-verlag, 1994, pp.125-150.

DOI: 10.1007/978-1-4612-2544-7_9

[4] Donoho DL, Johnstone IM, Threshold Selection For Wavelet Shrinkage of Noisy Data, Engineering In Medicine And Biology Society, 1994, Engineering Advances; New Opportunities for Biomedical Engineers. Proceedings of The 16th Annual International Conference of the IEEE, 3-6Nov 1994, 16(1), A24-25.

DOI: 10.1109/iembs.1994.412133

[5] Donoho DL. De-noising by soft-thresholding, . IEEE Translations on Information Theory, 1995, 41(3): pp.613-627.

DOI: 10.1109/18.382009

[6] Donoho DL, Johnstone IM, Wavelet Shrinkage: Asymptoia, Jounal of the Royal Satistical Society Series(B), 1995, 57: 301-369.

[7] Yan Liu; Peng Liu; Feihong Yu Denoising of Spectral Signal in MiniatureSpectrometer Based on Stationary Wavelet Transform, Photonics and Optoelectronics (SOPO), 2012 Symposium  p.1 – 4.

DOI: 10.1109/sopo.2012.6270442

[8] Yang Youliang; Guo Liwei; Li Fei"Application of wavelet transform in spectral data de-noising" System Science, Engineering Design and Manufacturing Informatization (ICSEM), 2011 International Conference pp.197-199.

DOI: 10.1109/icssem.2011.6081275

[9] Badiezadegan, S.; Rose, R. C , A wavelet-based data imputation approach to spectrogram reconstruction for robust speech recognition, Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference, pp.4780-4783.

DOI: 10.1109/icassp.2011.5947424