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ABSTRACT
In the quantitative analysis of spectral data, the noise often affects the accuracy of analytic results. In order to improve the analytic precision of the spectral data, the spectral signal collected with CCD need to denoise. According to the characteristics of spectral signal and combining with the advantages of the hard threshold function and the soft threshold function, we propose an improved threshold function denoising algorithm(ITFDA), which can not only overcome the discontinuity of hard threshold, but also overcome the constant error of soft threshold. The simulation experiments conducted with the improved threshold function are compared with the hard and soft threshold in terms of Signal to Noise Ratio(SNR) and Mean Square Error(MSE). The results show that the proposed denoising algorithm with improved threshold function can remove noise from the spectral signal in a certain extent, retain the important characteristics spectrum - peak of spectral signal, and reconstruct the spectral signal very well.
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REFERENCES
[1] Tai-Chiu Hsung, Daniel Pak-Kong Lun, and Wan-Chi Siu. A Deblocking Technique for Block-Transform Compressed Image Using Wavelet Transform Modulus Maxima. IEEE Transactions on Image Processing, Vol.7, No.10, 1998, pp. 1488-1496.
[2] Liu Li-mei, Liu Qi-yue, and Zhang Jing. Research in De-noising Method Base on Wavelet Transform Modulus Maxima. Hebei Journal of Industrial Science and Technology, Vol.27, No.6, 2010, pp. 367-372.
[3] Lei Zhang, Paul Bao. Denoising by Spatial Correlation Thresholding. IEEE Transactions on Circuits and Systems for Video Technoloy, Vol.13, No.6, 2003, pp. 535-538.
[4] Yuan Li-ying, Zhang Rui, Xu Peng, et al. Research on Adaptive Threshold Desoising Method Based on Correlation. Journal of Harbin University of Commerce(Natural Sciences Edition), Vol.29, No.5, 2013, pp. 571- 574.
[5] David L. Donoho, Iain M. Johnstone. Ideal Spatial Adaptation by Wavelet Shrinkage
[J]. Biometrika，Vol.81, No.3, 1994，pp. 425-455.
[6] Donoho, D.L. De-noising by soft-thresholding. IEEE Transactions on Information Theory, Vol.41, No.3, 1995，pp. 613-627.
[7] Zhao Yinshan, Turghunjan Abdukirim turki. Denoising Method of Wavelet Threshold Function Improvement. Computer Engineering and Applications, Vol.49, No.22, 2013, pp. 212-214.
[8] Wang Bei, Zhang Genyao, Li Zhi, et al. Wavelet Threshold Denoising Algorithm Based on New Threshold Function. Journal of Computer Applications, Vol.34, No.5, 2014, pp. 1499-1502.
[9] Abdolhossein Fathi and Ahmad Reza Naghsh- Nilchi. Efficient Image Denoising Method Based on a New Adaptive Wavelet Packet Thresholding Function. IEEE Transactions on Image Processing, vol.21, No.9, 2012, pp. 3981-3990.
[10] P Nirmala Devi, R Asokan. An improved adaptive wavelet shrinkage for ultrasound despeckling. Sadhana - Academy Proceedings in Engineering Sciences, Vol.39, No.4, 2014, pp. 971-988.
[11] Lige Yu. Research on Spectral Data Acquisition System. Applied Mechanics and Materials, Vol.602-605, 2014, pp. 3056-3059.
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