Research And Application Of Wavelet Transform On Spectroscopy And Multi-spectral Image

The dissertation is divided into two parts:applications of wavelet transform on spectral quantitative analysis and multi-spectral digital image.Wavelet transform(WT)is a new mathematical method.Recently,it is applied on spectral quantitative analysis extensively.In this thesis,firstly,an introduction of wavelet transform and multi-resolution analysis is presented.Then,WT is applied on spectral noise removal and extraction of useful information,good results are obtained.The second part in the dissertation is combination of multi-spectral digital image technology and wavelet transform to extract the edge information,and the extracted edge information is applied on the restoration with blind deconvolution,which restrained the ringing artifacts effectively.The detailed content and the main research results are as followed:(1)Wavelet threshold de-noising includes hard-and soft-threshold strategy.The hard-threshold may lead to the oscillation,and soft threshold may cause constant deviations between estimated wavelet coefficients and original signal wavelet coefficients.An improved de-noising method was proposed to solve the defects. According to the characteristics of different signal,a flexible parameter in the improve method was adjusted automatically.Simultaneously,other parameters such as wavelet function,decomposition level and threshold estimation method affects seriously the quality of de-noising.So,the simulated annealing algorithm (SAA)was used to find the optimal parameters.The presented method was applied on visible-near infrared(vis-near)spectra of milk powder.The results showed the obtained parameters by SAA were optimal parameters.(2)The new algorithm for frequency derangement of wavelet packet transform(WTP) is presented.The new algorithm has not changed the original algorithm of WTP, and applied on vis-near spectral of glycerol gonolaurate and lubricant,The new algorithm could explain clearly the physical meaning of WTP for spectral.(3)In the decomposition domain of WPT,the frequency sub-bands who contribute to the calibration model have different.In this dissertation,SAA was adopted to find the sub-band who contributes most to model.The proposed method was applied on vis-near spectral of glycerol gonolaurate and lubricant.The model was built by partial least square(PLS)regress.Compared to the model using the whole original spectra,the root mean square error of prediction(RMSEP)of glycerol gonolaurate and lubricant was improved from 7.9557 to 6.6787,and 0.2383 to 0.1031 respectively.(4)In uninformative variable elimination(UVE)algorithm,the cutoff threshold of UVE is evaluated by adding a noise matrix which size is the same with instrumental response data.The method to evaluate the cutoff is experientially and randomly,and difficult to obtain the optimal cutoff threshold.In this dissertation,the improved UVE(IUVE)was presented.In the IUVE,the SAA was used to find the optimal cutoff threshold,and applied on Vis-near spectral of glycerol gonolaurate and lubricant.Compared to the traditional UVE,RMSEP of gonolaurate and lubricant obtained by IUVE was improved from 7.3171 to 7.0171,and 0.1044 to 0.0991 respectively.Simultaneously,in WPT decomposition domain,only a few coefficients may explain the information of the whole original spectra.If WPT coefficients are used in the IUVE,more parsimonious model should be obtained.The proposed method was applied on the vis-near spectral of glycerol gonolaurate and lubricant.Compared to IUVE, the result indicated that the input variables for calibration model was reduced from 319 to 164 for glycerol gonolaurate,and 472 to 13 for lubricant,and the prediction precision was not compromised.(5)The three channel of MS3100 multi-spectral imager are green(Gn),near infrared (Ir),and red(Rd).Ir-channel image is particularly suitable for distinction of the crop and background objects(such as soil,etc.),so it is extremely beneficial to weeds recognition.The shape is important feature for distinction of weeds and crops,so the edge information should be extracted in order to recognize the weeds and crops correctly.In the dissertation,integration of B-spline wavelet transform and multi-spectral image technology was used to extract the edge information of multi-spectral image,the results showed the important edge information is extracted by the proposed method.Finally,the edge information was applied on the blind revolution restoration,and the ringing artifacts.were eliminated effectively.