Sub-optimal wavelet denoising of coaveraged spectra employing statistics from individual scans

This paper proposes a novel wavelet denoising method, which exploits the statistics of individual scans acquired in the course of a coaveraging process. The proposed method consists of shrinking the wavelet coefficients of the noisy signal by a factor that minimizes the expected square error with respect to the true signal. Since the true signal is not known, a sub-optimal estimate of the shrinking factor is calculated by using the sample statistics of the acquired scans. It is shown that such an estimate can be generated as the limit value of a recursive formulation. In a simulated example, the performance of the proposed method is seen to be equivalent to the best choice between hard and soft thresholding for different signal-to-noise ratios. Such a conclusion is also supported by an experimental investigation involving near-infrared (NIR) scans of a diesel sample. It is worth emphasizing that this experimental example concerns the removal of actual instrumental noise, in contrast to other case studies in the denoising literature, which usually present simulations with artificial noise. The simulated and experimental cases indicate that, in classic denoising based on wavelet coefficient thresholding, choosing between the hard and soft options is not straightforward and may lead to considerably different outcomes. By resorting to the proposed method, the analyst is not required to make such a critical decision in order to achieve appropriate results.     

Determination of Total Sugar in Tobacco by Near-Infrared Spectroscopy and Wavelet Transformation-Based Calibration

Based on wavelet transformation (WT) and mutual information (MI), a simple and effective procedure is proposed for multivariate calibration of near-infrared spectroscopy. In such a procedure, the original spectra of the training set are first transformed into a set of wavelet representations by wavelet prism transform. Then, the MI value between each wavelet coefficient variable and the dependent variable is calculated, resulting in a MI spectrum; by retaining a subset set of coefficients with higher MI, an update training set consisting of wavelet coefficients is obtained and reconstructed/converted back to the original domain. Based on this, a partial least square (PLS) model can be constructed and optimized. The optimal wavelet and decomposition level are determined by experiment. A NIR quantitative problem involving the determination of total sugar in tobacco is used to demonstrate the overall performance of the proposed procedure, named RPLS, meaning PLS in reconstructed original domain coupled with MI-induced variable selection in wavelet domain (RPLS). Three kinds of procedures, that is, conventional full-spectrum PLS in original domain (FPLS), PLS in original domain coupled with MI-induced variable selection (OPLS), and direct PLS in MI-based wavelet coefficients (WPLS), are used as reference. The result confirms that it can build more accurate and robust calibration models without increasing the complexity.     

Curvelet based image compression via core vector machine

In this paper, a novel curvelet based digital image compression scheme is proposed. Aiming at achieving high compression ratio, the proposed scheme embeds a representative machine learning method, core vector machine (CVM), in the encoding process of the image compression technique. The core vector machine (CVM) has been introduced as an extremely fast classifier which is demonstrably superior to standard support vector machine (SVM) on very large datasets. In this scheme, we appropriately utilize the characteristic of CVM to reduce huge numbers of curvelet coefficients. Compared with image compression algorithms do not use CVM and methods based on wavelet transform, experimental results show that the compression performance of our method gains much improvement in peak-signal-to-noise-ratio (PSNR) and CPU time. Moreover, the algorithm works fairly well for declining block effect at higher compression ratios.     

Mathematical background for a method on quotient signal decomposition

The blind source separation problem is discussed in this article. Focusing on the assumption of independency of the sources in the time-frequency domain, we present a mathematical formulation for the estimation problem of the number of sources. The proposed method uses the quotient of complex valued time-frequency information of only two observed signals to detect the number of sources. No fewer number of observed signals than the detected number of sources is needed to separate sources. The assumption on sources is quite general independence in the time-frequency plane, which is different from that of independent component analysis. We propose algorithms with feedback and give numerical simulations to show the method works well even for noisy case.     

System identification based on distribution theory and wavelet transform

A review of system identification based on distribution theory is given. By the Schwartz kernel theorem, to every continuous linear system there corresponds a unique distribution, called kernel distribution. Formulae using wavelet transform to access time--frequency information of kernel distributions are deduced. A new wavelet-based system identification method for health monitoring systems is proposed as an application of a discretized formula using stationary wavelet transform.     

Aliasing Error of Sampling Series in Wavelet Subspaces

Alising error arises whenever a sampling formula, valid for a prescribed space, is applied to a function in a bigger space. In this work, we estimate the aliasing error of classic and average sampling expansions in wavelet subspaces of a multiresolution analysis.     

关于L2(Rd)中仿射子空间小波标架的一个注记

研究了L2(Rd)的有限生成仿射子空间中小波标架的构造.证明了任意有限生成仿射子空间都容许一个具有有限多个生成元的Parseval小波标架,并且得到了仿射子空间是约化子空间的一个充分条件.对其傅里叶变换是一个特征函数的单个函数生成的仿射子空间,得到了与小波标架构造相关的投影算子在傅里叶域上的明确表达式,同时也给出了一些例子.     

小波变换对OCT图像的降噪处理

通过分析光学相干层析成像系统中的噪音源以及不同噪音对成像质量的影响，利用小波变换的方法，结合软阈值滤波对图像去噪，消除噪音对图像的干扰，处理后图像变得清晰了，图像质量得以改善，表明该方法能达到减小图像噪音的目的.     

Modification of three dimensional topography of the machined KDP crystal surface using wavelet analysis method

The wavelet analysis method has been extensively employed to analyze the surface structures and evaluate the surface roughness. In this work, however, the wavelet analysis method was introduced to decompose and reconstruct the sampled surface profile signals in the cutting direction that achieved by SPDT (single point diamond turning) operation, and the surface profile signals in tool feeding direction were reconstructed with the approximate harmonic functions directly. And moreover, the orthogonal design method, i.e. the combination design of general rotary method, was resorted to model the variations of the independent frequency and amplitude of different simulated harmonic signals in the cutting and tool feeding directions. As expected resultantly, a novel 3D surface topography modeling solution was established, which aims to predict and modify the finished KDP (potassium dihydrogen phosphate or KH₂PO₄) crystal surfaces. The validation tests were carried out finally under different cutting conditions, and the collected average surface roughness in any case was compared with the corresponding value as predicted. The results indicated the experimental data were well consistent with the predictions, and only an average relative error of 11.4% occurred in predicting the average surface roughness.