Two-dimensional nonlinear wavelet compression of ion mobility spectra of chemical warfare agent simulants

Ion mobility spectrometry (IMS) affords miniaturized hand-held devices that can be used for monitoring and remote measurement. Because such instruments have limits on storage capacity or bandwidth for wireless transmission, data compression is important. Furthermore, all instruments should be operated with the fastest possible sampling rates because a signal-to-noise gain can be achieved by wavelet compression. Linear wavelet compression (LWC) applied to IMS data may cause peak distortion when the spectra are reconstructed. Nonlinear wavelet compression (NLWC) precisely preserves the peak location (i.e., drift time), height, and shape. IMS data of three chemical warfare simulants, dimethyl methylphosphonate, triethyl phosphate, and dipropyleneglycol monomethyl ether, were collected from an Ion Track ITEMISER and a Graseby Ionics detector CAM. Two-dimensional NLWC was used to compress the IMS data in the drift time and data acquisition dimensions on IMS data of chemical warfare simulants. NLWC was applied to achieve a compression factor of 1/128 with relative error of root-mean-square of <0.25% in the reconstructed spectra. A method was also developed and evaluated for optimizing compression.     

Nonlinear wavelet compression of ion mobility spectra from ion mobility spectrometers mounted in an unmanned aerial vehicle

Linear and nonlinear wavelet compression of ion mobility spectrometry (IMS) data are compared and evaluated. IMS provides low detection limits and rapid response for many compounds. Nonlinear wavelet compression of ion mobility spectra reduced the data to 4-5% of its original size, while eliminating artifacts in the reconstructed spectra that occur with linear compression, and the root-mean-square reconstruction error was 0.17-0.20% of the maximum intensity of the uncompressed spectra. Furthermore, nonlinear wavelet compression precisely preserves the peak location (i.e., drift time). Small variations in peak location may occur in the reconstructed spectra that were linearly compressed. A method was developed and evaluated for optimizing the compression. The compression method was evaluated with in-flight data recorded from ion mobility spectrometers mounted in an unmanned aerial vehicle (UAV). Plumes of dimethyl methylphosphonate were disseminated for interrogation by the UAV-mounted IMS system. The daublet 8 wavelet filter exhibited the best performance for these evaluations.     

Multivariate curve resolution of wavelet and Fourier compressed spectra

The multivariate curve resolution method SIMPLe to use Interactive Self-Modeling Mixture Analysis (SIMPLISMA) was applied to Fourier and wavelet compressed ion-mobility spectra. The spectra obtained from the SIMPLISMA model were transformed back to their original representation, that is, uncompressed format. SIMPULSMA was able to model the same pure variables for the partial wavelet transform, although for the Fourier and complete wavelet transforms, satisfactory pure variables and models were not obtained. Data were acquired from two samples and two different ion mobility spectrometry (IMS) sensors. The first sample was thermally desorbed sodium gamma-hydroxybutyrate (GHB), and the second sample was a liquid mixture of dicyclohexylamine (DCHA) and diethylmethylphosphonate (DEMP). The spectra were compressed to 6.3% of their original size. SIMPLISMA was applied to the compressed data in the Fourier and wavelet domains. An alternative method of normalizing SIMPLISMA spectra was devised that removes variation in scale between SIMPLISMA results obtained from uncompressed and compressed data. SIMPLISMA was able to accurately extract the spectral features and concentration profiles directly from daublet compressed IMS data at a compression ratio of 93.7% with root-mean-square errors of reconstruction < 3%. The daublet wavelet filters were selected, because they worked well when compared to coiflet and symmlet. The effects of the daublet filter width and compression ratio were evaluated with respect to reconstruction errors of the data sets and SIMPLISMA spectra. For these experiments, the daublet 14 filter performed well for the two data sets.     

Application of linear and nonlinear discrete wavelet transforms to MALDI-MS measurements of bacteria for classification

The linear and nonlinear discrete wavelet transforms (DWTs) were used to compress matrix-assisted laser desorption/ionization mass spectra to address two key challenges: the relatively high noise level and the underdetermined format of the data set. By applying the DWT to MALDI-MS spectra, the spectra were simultaneously smoothed and compressed. Multivariate projected difference resolution was used to evaluate the effects of the linear and nonlinear DWT on classification. The cross-validation study using bootstrapped Latin partition and partial least-squares (PLS-2) has proved that the classification accuracy increased after data compression. The best result was obtained when using Fisher's criterion to choose wavelet coefficients for compression. With the aid of principal component analysis (PCA), different wavelet filters may provide different mathematical perspectives to visualize the clustering of bacteria. The effect of growth time was directly observed with wavelet transform, which could not be observed using the original spectra.     

Compression of interference patterns with application to phase-shifting digital holography

A compression method of phase-shifting digital holographic data is presented. Three interference patterns are recorded, and holographic information is extracted from them by phase-shifting interferometry. The scheme uses standard baseline Joint Photographic Experts Group (JPEG) or standard JPEG-2000 image compression techniques on the recorded interference patterns to reduce the amount of data to be stored. High compression rates are achieved for good reconstructed object image quality. The utility of the proposed method is experimentally verified with real holographic data. Results for compression rates using JPEG-2000 and JPEG of approximately 27 and 20, respectively, for a normalized root-mean-square error of approximately 0.7 are demonstrated.     

基于生成对抗网络的压缩感知图像重构方法

目的为了解决传统压缩感知图像重构方法存在的重构时间长、重构图像质量不高等问题,提出一种基于生成对抗网络的压缩感知图像重构方法。方法基于生成对抗网络思想设计一种由具有稀疏采样功能的鉴别器和具有图像重构功能的生成器组成的深度学习网络模型,利用对抗损失和重构损失2个部分组成的新的损失函数对网络参数进行优化,完成图像压缩重构过程。结果实验表明,文中方法在12.5%的低采样率下重构时间为0.009s,相较于常用的OMP算法、CoSaMP算法、SP算法和IRLS算法,其峰值信噪比(PSNR)提高了10～12 dB。结论文中设计的方法应用于图像重构时重构时间短,在低采样率下仍能获得高质量的重构效果。     

Systematic fatigue spectrum editing by fast wavelet transform and genetic algorithm

Fatigue testing is critical in order to establish the service life of load-bearing components and structures. The extensive time associated with full fatigue spectrum testing can lead to prohibitive costs. A significant need exists for a fatigue load spectrum editing methodology, based on the mechanics of fatigue, that produces load spectra that can replicate service damage in laboratory testing and can lead to compressed testing times and reduced costs. In this work, a wavelet genetic (WAVEGEN) algorithm is developed to edit fatigue loading spectra using wavelet analysis to greatly reduce the length of a spectrum while retaining the same damage accumulation characteristics. In addition, an optimization protocol using a genetic algorithm is included within this process to automatically select the best wavelet editing parameters. The algorithm is designed to identify the most suitable wavelet type, filter, and level to optimally edit a given fatigue spectrum and ensure equivalence between edited and unedited spectra from a damage perspective. The algorithm was applied to two well-known aircraft fatigue spectra: Fighter Aircraft Loading Standard for Fatigue evaluation (FALSTAFF) and Transport Wing Standard (TWIST). The proposed approach has demonstrated that both spectra can be compressed significantly even while ensuring equivalence from a damage perspective.     

扩展方向树的遥感图像小波包压缩算法

针对遥感图像纹理丰富、空间相关性弱,普通压缩算法容易造成高频信息丢失的特点,本文利用小波包优良的高频分析能力,提出一种结合SPIHT的小波包编码算法.该算法采用类似SPIHT算法的零树结构,通过重新定义方向树,即扩展方向树,改变小波包各节点之间的对应关系,解决小波包分解时产生的"父冲突"问题.同时,对扩展方向树的合理性进行了实验验证,并结合SPIHT算法实现了整个编解码.实验结果表明,对于富含纹理的遥感图像,在1bpp的压缩率下,该算法峰值信噪比(PSNR)超出SPIHT算法0.5-1dB,且视觉效果更好.     

Three-dimensional image compression with integer wavelet transforms

A three-dimensional (3-D) image-compression algorithm based on integer wavelet transforms and zerotree coding is presented. The embedded coding of zerotrees of wavelet coefficients (EZW) algorithm is extended to three dimensions, and context-based adaptive arithmetic coding is used to improve its performance. The resultant algorithm, 3-D CB-EZW, efficiently encodes 3-D image data by the exploitation of the dependencies in all dimensions, while enabling lossy and lossless decompression from the same bit stream. Compared with the best available two-dimensional lossless compression techniques, the 3-D CB-EZW algorithm produced averages of 22%, 25%, and 20% decreases in compressed file sizes for computed tomography, magnetic resonance, and Airborne Visible Infrared Imaging Spectrometer images, respectively. The progressive performance of the algorithm is also compared with other lossy progressive-coding algorithms.     

采用小波与神经网络的高效图像数据压缩方案

通过构造特别的映射、整函数和BP神经网络,获得一套基于神经网络的无损数据压缩方案。由于该方案能压缩已被小波编码压缩过的数据,因此将其嵌套入一好的小波编码系统就可以获得一种基于小波与神经网络的高效图像数据压缩方案。实验证明,该高效方案对于Lenna图像的压缩比为43∶1, 并且恢复的图像有较好的视觉效果。     

一种基于小波子带熵的遥感图像压缩算法

提出了一种使用小波子带熵进行比特分配的遥感图像压缩算法.对遥感图像进行小波提升分解后,分析了各高频子带能量百分比及其熵的变化趋势,在此基础上提出了一种新的快速比特分配方法-使用子带熵进行比特分配.然后对各个高频子带进行均匀量化,量化后的数据采用比特平面编码.对最高比特平面只记录该比特平面中非零系数的坐标,其它比特平面采用行程编码和Huffman编码方法进行压缩.实验结果表明,纹理复杂以及相对平坦的遥感图像使用该算法压缩后都可以获得很好的重构图像质量,峰值信噪比均大于34dB,而压缩比则与图像的复杂程度有关.     

小波双三次插值搜索算法提高遥感图像分辨力

通过小波双三次插值中高频外推阈值门限选取与峰值信噪比变化关系的分析,提出了小波双三次插值搜索算法。该算法能够自动搜索到高频外推的最佳阈值门限,在不破坏光学遥感图像原始信息的情况下,提高图像的空间分辨力和峰值信噪比,有利于对图像的细节信息进行观察分析。实验表明,该算法的重建图像的峰值信噪比比全小波插值图像和小波双线性插值图像的峰值信噪比分别高6.5dB和2.4dB,熵提高到原图像的1.3倍,是一种提高光学遥感图像分辨力的有效算法。     

Artifact reduction of compressed color images using modified mean‐removed classified vector quantization

Abstract

In this paper, a new artifact reduction algorithm for compressed color images using MMRCVQ is proposed. The algorithm extends and modifies vector quantization (VQ) for discovering the relationships between the uncompressed color images and their deblocked compressed versions by classifying the deblocked compressed blocks into several categories using information from their neighboring blocks. The discovered relationships are stored in two codebooks and are used to recover the missing information of compressed color images. To increase the availability of codewords and reduce the memory needed for storing codewords, mean‐removed vectors are used to generate codebooks. The experimental results show that the proposed approach can remove, effectively, the artifacts caused by high compression and improve perceptual quality significantly. Compared to existing methods, the proposed approach usually uses much less computing time to recover a compressed color image and has much better image quality.     

一种采用多个小波基的图像融合去噪方法

提出了一种新的基于多个小波基的图像融合去噪方法.首先利用多个不同的小波基对含噪图像进行阈值去噪,得到多幅恢复图像.然后对这些图像采用小波融合方法进行融合.对于低频系数采用基于边缘的融合算法,在多幅恢复图像中选择最有可能是边缘的点加以保留;对于高频系数,采用了平均的融合算法.最后得到一幅去噪图像.实验结果表明,无论是在视觉效果上还是在峰值信噪比定量指标上该方法去噪效果均明显优于单一小波基去噪.     

Application of two-dimensional correlation spectroscopy to chemometrics: self-modeling curve resolution analysis of spectral data sets

This paper demonstrates the use of two-dimensional (2D) correlation spectroscopy in conjunction with alternating least squares (ALS) based self-modeling curve resolution (SMCR) analysis of spectral data sets. This iterative regression technique utilizes the non-negativity constraints for spectral intensity and concentration. ALS-based SMCR analysis assisted with 2D correlation was applied to Fourier transform infrared (FT-IR) spectra of a polystyrene/methyl ethyl ketone/deuterated toluene (PS/MEK/d-toluene) solution mixture during the solvent evaporation process to obtain the pure component spectra and then the time-dependent concentration profiles of these three components during the evaporation process. We focus the use of asynchronous 2D correlation peaks for the identification of pure variables needed for the initial estimates of the ALS process. Choosing the most distinct bands via the positions of asynchronous 2D peaks is a viable starting point for ALS iteration. Once the pure variables are selected, ALS regression can be used to obtain the concentration profiles and pure component spectra. The obtained pure component spectra of MEK, d-toluene, and PS matched well with known spectra. The concentration profiles for components looked reasonable.     

基于小波融合的电容层析成像图像重建

提出了基于小波变换的电容层析成像重建图像融合方法。首先,使用共轭梯度最小二乘法算法及Landweber迭代算法分别进行图像重建;其次,将所得重建图像进行小波分解,其近似分量按加权平均的融合规则进行处理,细节分量按绝对值最大融合规则进行处理;最后,将融合之后的数据进行小波重构,获得新的重建图像。仿真及实验结果表明,融合后的重建图像精度有所提高、图像伪影明显减少。     

A hybrid contextual compression technique using wavelet and contourlet transforms with PSO optimized prediction

Contextual compression is an essential part of any medical image compression since it facilitates no loss of diagnostic information. Although there are many techniques available for contextual image compression still there is a need for developing an efficient and optimized technique which would produce good quality images at lower bit rates. This article presents an efficient contextual compression algorithm using wavelet and contourlet transforms to capture the fine details of the image, along with directional information to produce good quality at high Compression Ratio (CR). The 2D discrete wavelet transform, which uses the simplest Daubechies wavelets, db1, or haar wavelet, is chosen and used to get the subband coefficients. The approximate coefficients of the higher subbands undergo contourlet transform employing length N ladder filters for capturing the directional information of the subbands at different scale and orientations. An optimized approach is used for predicting the quantized and the normalized subband coefficients resulting in improved compression performance. The proposed contextual compression approach was evaluated for its performance in terms of CR, Peak Signal to Noise Ratio, Feature SIMilarity index, Structure SIMilarity Index, and Universal quality (Q) after reconstruction. The results clarify the efficiency of the proposed method over other compression techniques.     

基于模极大值和尺度理论的音频降噪方法

在数字化时代,音频的转录或录制都会引入噪音,但是历史音频保存和音频资料处理需要纯净的音频信号,因此音频降噪研究有着重要的现实意义。该文首先介绍了二进小波和奇异性指数,并阐述了尺度跟踪和模极大值重构等理论,在Mallat工作的基础上,提出了一种基于小波滤波的音频降噪方法。该方法首先引入补偿因子削减二进小波变换对系数造成的影响,并计算带噪音频的小波系数和模极大值;然后基于信号和噪声奇异指数不同的特点,结合阈值降噪和尺度跟踪理论,采用层间相关搜索去除噪声的模极大值;最后利用交替投影算法,重建音频信号。本文用该方法处理带click和hiss噪声的音频信号,跟小波阈值方法和小波包方法相比,能达到较好的听觉效果和信噪比。同时观察信号的波形图及模极大值演示图,发现本方法都表现出优异的降噪效果。     

Suppression of the zero-order diffracted beam from a pixelated spatial light modulator by phase compression

Phase compression is used to suppress the on-axis zero-order diffracted (ZOD) beam from a pixelated phase-only spatial light modulator (SLM) by a simple modification to the computer generated hologram (CGH) loaded onto the SLM. After CGH design, the phase of each SLM element is identically compressed by multiplying by a constant scale factor and rotated on the complex unit-circle to produce a cancellation beam that destructively interferes with the ZOD beam. Experiments achieved a factor of 3 reduction of the ZOD beam using two different liquid-crystal SLMs. Numerical simulation analyzed the reconstructed image quality and diffraction efficiency versus degree of phase compression and showed that phase compression resulted in little image degradation or power loss.     

Increased identification of peptides by enhanced data processing of high-resolution MALDI TOF/TOF mass spectra prior to database searching

This paper presents application of sequential enhanced data processing procedures to high-resolution tandem mass spectra for identification of peptides using the Mascot database search algorithm. A strategy for (1) selection of fragment ion peaks from MS/MS spectra, (2) utilization of improved mass accuracy of the precursor ions, and (3) wavelet denoising of the mass spectra prior to fragment ion selection have been developed. The number of peptide identifications obtained using the enhanced processing was then compared with that obtained using software provided by the instrument manufacturer. Approximately 9000 MS/MS spectra acquired by the Applied Biosystems 4700 TOF/TOF MS instrument were used as a model data set. After application of the new processing, an increase of 33% unique peptides and 22% protein identifications with at least two unique peptides were found. The influence of the processing on the percentage of false positives, estimated by searching against a randomized database, was estimated to increase false positive identifications from 2.7 to 3.9%, which was still below the 5% error rate specified in the Mascot search. These data processing approaches increase the amount of information that can be extracted from LC-MS analysis without the necessity of additional experiments.