面向高保真再现的多光谱图像融合技术

面向高保真再现(高保真显示和高保真印刷)的多光谱图像融合是多光谱颜色再现的关键技术和核心环节.论文结合人类视觉系统的构成与特性,采用基于多分辨率分析理论的图像融合方法,并嵌入基于图像色貌模型的色彩转换方法,提出了面向高保真再现的多光谱图像融合方法,其核心为基于人类视觉系统的小波图像融合方法,并设计了融合框架、融合算法和融合效果评价指标.最后通过高分辨率图像与多光谱图像的融合试验,并通过融合指标的分析计算验证了此技术方法的有效性,它为颜色视觉的阶段理论学说提供了新的理论解释.     

Denoising and multivariate analysis of time‐of‐flight SIMS images

Time‐of‐flight SIMS (ToF‐SIMS) imaging offers a modality for simultaneously visualizing the spatial distribution of different surface species. However, the utility of ToF‐SIMS datasets may be limited by their large size, degraded mass resolution and low ion counts per pixel. Through denoising and multivariate image analysis, regions of similar chemistries may be differentiated more readily in ToF‐SIMS image data. Three established denoising algorithms—down‐binning, boxcar and wavelet filtering—were applied to ToF‐SIMS images of different surface geometries and chemistries. The effect of these filters on the performance of principal component analysis (PCA) was evaluated in terms of the capture of important chemical image features in the principal component score images, the quality of the principal component score images and the ability of the principal components to explain the chemistries responsible for the image contrast. All filtering methods were found to improve the performance of PCA for all image datasets studied by improving capture of image features and producing principal component score images of higher quality than the unfiltered ion images. The loadings for filtered and unfiltered PCA models described the regions of chemical contrast by identifying peaks defining the regions of different surface chemistry. Down‐binning the images to increase pixel size and signal was the most effective technique to improve PCA performance. Copyright © 2003 John Wiley & Sons, Ltd.     

Image fusion combining SEM and ToF‐SIMS images

Image fusion allows for the combination of an image containing chemical information but low spatial resolution with a high‐spatial resolution image having little to no chemical information. The resulting hybrid image retains all the information from the chemically relevant original image, with improved spatial resolution allowing for visual inspection of the spatial correlations. In this research, images were obtained from two sample test grids: one of a copper electron microscope grid with a letter ‘A’ in the center (referred to below as the ‘A‐grid’), and the other a Tantalum and Silicon test grid from Cameca that had an inscribed letter ‘C’ (referred to below as the ‘Cameca grid’). These were obtained using scanning electron microscopy (SEM) and time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). Image fusion was implemented with the Munechika algorithm. The edge resolution of the resulting hybrid image was calculated compared with the edge resolution obtained for both the individual ToF‐SIMS and SEM images. The challenges of combining complimentary datasets from different instrumental analytical methods are discussed as well as the advantages of having a hybrid image. The distance across the edge for hybrid images of the A‐Grid and the Cameca grid were determined to be 21 µm and 8 µm, respectively. When these values were compared to the original ToF‐SIMS, SEM and optical microscopy measurements, the fused image had a spatial resolution nearly equal to that obtained in the SEM image for both samples. Copyright © 2014 John Wiley & Sons, Ltd.     

Multispectral optical imaging combined in situ with XPS or ToFSIMS and principal component analysis

All X‐ray photoelectron spectroscopy (XPS) and time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) instruments have optical cameras to image the specimen under analysis, and often to image the sample holder as it enters the system too. These cameras help the user find the appropriate points for analysis of specimens. However they seldom give as good images as stand‐alone bench optical microscopes, because of the limited geometry, source/analyser solid angle and ultra‐high‐vacuum (UHV) design compromises. This often means that the images displayed to the user necessarily have low contrast, low resolution and poor depth‐of‐field. To help identify the different regions of the samples present we have found it useful to perform multispectral imaging by illuminating the sample with narrow‐wavelength‐range light emitting diodes (LEDs). By taking an image under the illumination of these LEDs in turn, each at a successively longer wavelength, one can build up a set of registered images that contain more information than a simple Red–Green–Blue image under white‐light illumination. We show that this type of multispectral imaging is easy and inexpensive to fit to common XPS and ToF‐SIMS instruments, using LEDs that are widely available. In our system we typically use 14 LEDs including one emitting in the ultraviolet (so as to allow fluorescent imaging) and three in the near infra‐red. The design considerations of this system are discussed in detail, including the design of the drive and control electronics, and three practical examples are presented where this multispectral imaging was extremely useful. Copyright © 2016 The Authors Surface and Interface Analysis Published by John Wiley & Sons Ltd.     

Automated correlation and classification of secondary ion mass spectrometry images using a k-means cluster method

We present a novel method for correlating and classifying ion-specific time-of-flight secondary ion mass spectrometry (ToF-SIMS) images within a multispectral dataset by grouping images with similar pixel intensity distributions. Binary centroid images are created by employing a k-means-based custom algorithm. Centroid images are compared to grayscale SIMS images using a newly developed correlation method that assigns the SIMS images to classes that have similar spatial (rather than spectral) patterns. Image features of both large and small spatial extent are identified without the need for image pre-processing, such as normalization or fixed-range mass-binning. A subsequent classification step tracks the class assignment of SIMS images over multiple iterations of increasing n classes per iteration, providing information about groups of images that have similar chemistry. Details are discussed while presenting data acquired with ToF-SIMS on a model sample of laser-printed inks. This approach can lead to the identification of distinct ion-specific chemistries for mass spectral imaging by ToF-SIMS, as well as matrix-assisted laser desorption ionization (MALDI), and desorption electrospray ionization (DESI).     

Fractal analysis of polyethylene catalysts surface morphologies based on wavelet transform modulus maxima method

Surface morphologies of supported polyethylene (PE) catalysts are investigated by an approach combining fractal with wavelet. The multiscale edge (detail) pictures of catalyst surface are extracted by wavelet transform modulus maxima (WTMM) method. And, the distribution of edge points on the edge image at every scale is studied with fractal and multifractal method. Furthermore, the singularity intensity distribution of edge points in the PE catalyst is analyzed by multifractal spectrum based on WTMM. The results reveal that the fractal dimension values and multifractal spectrums of edge images at small scales have a good relation with the activity and surface morphology of PE catalyst. Meanwhile the catalyst exhibiting the higher activity shows the wider singular strength span of multifractal spectrum based on WTMM, as well as the more edge points with the higher singular intensity. The research on catalyst surface morphology with hybrid fractal and wavelet method exerts the superiorities of wavelet and fractal theories and offers a thought for studying solid surfaces morphologies. Supported by the Chinese Petroleum & Chemical Corporation Development Department (Grant No. x504024)     

Extraction of hidden information of ToF‐SIMS data using different multivariate analyses

Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) is a powerful tool for determining surface information of complex systems such as polymers and biological materials. However, the interpretation of ToF‐SIMS raw data is often difficult. Multivariate analysis has become effective methods for the interpretation of ToF‐SIMS data. Some of multivariate analysis methods such as principal component analysis and multivariate curve resolution are useful for simplifying ToF‐SIMS data consisting of many components to that explained by a smaller number of components. In this study, the ToF‐SIMS data of four layers of three polymers was analyzed using these analysis methods. The information acquired by using each method was compared in terms of the spatial distribution of the polymers and identification. Moreover, in order to investigate the influence of surface contamination, the ToF‐SIMS data before and after Ar cluster ion beam sputtering was compared. As a result, materials in the sample of multiple components, including unknown contaminants, were distinguished. Copyright © 2014 John Wiley & Sons, Ltd.     

Image enhancement using virtual contrast image fusion on Fe3O4 and ZnO nanodispersed decyloxy benzoic acid

Low contrast and noisy photographic pictures can be considerably improved by image-processing techniques. Techniques like histogram equalisation produce high-contrast images but often fail to preserve the colour texture information. To overcome this deficiency, a contrast-enhancement approach has been devised, using virtual contrast image fusion (in Haar wavelet domain). This technique has been evaluated by a study of the optical textures of nano-dispersed decyloxybenzoic acid with small quantities of Fe₃O₄ and ZnO added.     

Robust automated three-dimensional segmentation of secondary ion mass spectrometry image sets

Three-dimensional (3-D) element distributions generated by scanning secondary ion mass spectrometry (SIMS) are usually noisy and blurred and contain objects which do not usually have sharp edges or may have noise induced boundaries. Additionally, there are local intensity differences due to sensitivity differences of the channelplate. As a result, traditional segmentation techniques become difficult and yield rather poor results. We present a novel methodology which combines a restoration process (using a combination of channelplate sensitivity compensation with a 3-D de-noising technique based on the wavelet transform) with a fuzzy logic 3-D gray level segmentation which can be used to successfully segment 3-D SIMS image sets. The restoration algorithm removes the artifacts produced by the channelplate inhomogeneities as well as noise aberrations from the image sets and the gray level thresholding algorithm segments their features. The algorithm is designed for minimal user interaction to achieve a high automation level. The methodology is discussed and experimental results using real 3-D images are presented.     

Mitigating dead‐time effects during multivariate analysis of ToF‐SIMS spectral images

ToF‐SIMS spectra are formed by bombarding a surface with a pulse of primary ions and detecting the resultant ionized surface species using a time‐of‐flight mass spectrometer. Typically, the detector is a time‐to‐digital converter. Once an ion is detected using such detectors, the detector becomes insensitive to the arrival of additional ions for a period termed as the (detector) dead‐time. Under commonly used ToF‐SIMS data acquisition conditions, the time interval over which ions arising from a single chemical species reach the detector is on the order of the detector dead‐time. Thus, only the first ion reaching the detector at any given mass is counted. The event registered by the data acquisition system, then, is the arrival of one or more ions at the detector. This behavior causes ToF‐SIMS data to violate, in the general case, the assumption of linear additivity that underlies many multivariate statistical analysis techniques. In this article, we show that high‐mass‐resolution ToF‐SIMS spectral‐image data follow a generalized linear model, and we propose a data transformation and scaling procedure that enables such data sets to be successfully analyzed using standard methods of multivariate image analysis. Copyright © 2008 John Wiley & Sons, Ltd.     

Organic ion imaging of biological tissue with secondary ion mass spectrometry and matrix-assisted laser desorption/ionization

Organic secondary ion mass spectrometry (SIMS) and matrix-assisted laser desorption/ionization (MALDI) mass spectrometry can be used to produce molecular images of samples. This is achieved through ionization from a clearly identified point on a flat sample, and performing a raster of the sample by moving the point of ionization over the sample surface. The unique analytical capabilities of mass spectrometry for mapping a variety of biological samples at the tissue level are discussed. SIMS provides information on the spatial distribution of the elements and low molecular mass compounds as well as molecular structures on these compounds, while MALDI yields spatial information about higher molecular mass compounds, including their distributions in tissues at very low levels, as well as information on the molecular structures of these compounds. Application of these methods to analytical problems requires appropriate instrumentation, sample preparation methodology, and a data presentation usually in a three-coordinate plot where x and y are physical dimensions of the sample and z is the signal amplitude. The use of imaging mass spectrometry is illustrated with several biological systems.     

Multitechnique characterization of thin films of immiscible polymer systems: PS–b‐PMMA diblock copolymers and PS–PMMA symmetric blends

Immiscible polymer systems are known to form various kinds of phase‐separated structures capable of producing self‐assembled patterns at the surface. In this study, different surface characterization methods were utilized to study the surface morphology and composition produced after annealing thin polymer films. Two different SIMS techniques—static time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) and dynamic nano‐SIMS—were used, complemented by x‐ray photoelectron spectrometry (XPS) and atomic force microscopy (AFM). Thin films (spin‐coated onto silicon wafers) of polystyrene (PS)–poly(methyl methacrylate) (PMMA) symmetric blends and diblock copolymers of similar molecular weight were investigated. Surface enrichment by PS was found on all as‐cast samples. The samples were annealed at 160 °C for different time periods, after which the blend and the copolymer films exhibited opposite behaviour as seen by ToF‐SIMS and XPS. The annealed blend surface presented an increase in the PMMA concentration whereas that of copolymers showed a decrease in PMMA concentration compared with the as‐cast sample. For blends, the nano‐SIMS as well as AFM images revealed the formation of phase‐separated domains at the surface. The composition information obtained from ToF‐SIMS and XPS, as well as the surface mapping by nano‐SIMS and AFM, allowed us to conclude that PS formed phase separated droplet‐like domains on a thin PMMA matrix on annealing. The three‐dimensional nano‐SIMS images showed that the PS droplets were supported inside a rim of PMMA and that these droplets continued from the surface like columnar rods into the film until the substrate interface. In the case of annealed copolymer samples, the AFM images revealed topographical features resembling droplet‐like domains on the surface but there was no phase difference between the domains and the matrix. In the case of copolymers, owing to the covalent bonding between the blocks, complete phase separation was not possible. The three‐dimensional nano‐SIMS images showed domain structures in the form of striations inside the film, which were not continuous until the substrate interface. Information from the different techniques was required to gain an accurate view of the surface composition and topographical changes that have occurred under the annealing conditions. Copyright © 2005 John Wiley & Sons, Ltd.     

Wavelet transform of quantum chemical states

By using the technique of integral within an ordered product of operators, we recast classical wavelet transform to a matrix element of the squeezing‐displacing operator U(μ,s) between the mother wavelet vector 〈ψ| and the state vector |f〉 to be transformed, i.e., we propose that 〈ψ|U(μ,s)|f〉 can be considered as a new kind of spectrum for analyzing the quantum state |f〉. In this way, we propose the wavelet‐ transform spectrum for quantum chemical states. As an example, we carry out the numerical calculation of wavelet‐transform spectrum for the binomial state. It seems to us that this kind of spectrum can be used to recognize a variety of quantum chemical states. © 2011 Wiley Periodicals, Inc. Int J Quantum Chem, 2012     

ToF‐SIMS multivariate characterization of surface modification of polymers by N2?H2 atmospheric pressure dielectric barrier discharge

Time of flight secondary ion mass spectrometry (ToF‐SIMS) is a powerful tool for the surface characterization of plasma‐modified surface. However, the SIMS fragmentation patterns of the resulting surface are quite complex and a full interpretation may be prohibitive. As a result, many studies are turning to multivariate statistical methods to simplify the interpretation. In this study, a principal component analysis (PCA) was used to obtain a more detailed interpretation of the surface modification of polymers by an atmospheric pressure plasma. The dataset was obtained from three polymers with different chemical compositions [namely, polyethylene, polyvinylidene fluoride, and poly(tetrafluoroethylene)], where each material was treated with an atmospheric pressure dielectric barrier discharge (DBD) in an atmosphere composed of different N₂/H₂ ratios. The results are discussed in terms of the suitability of ToF‐SIMS analysis combined with PCA for the discrimination between the three polymers and the possibility to create a predictive model that would describe the plasma surface modification, independent of the polymer substrate chemical composition. Copyright © 2010 John Wiley & Sons, Ltd.     

N‐way modeling for wavelet filter determination in multivariate image analysis

When trying to analyze spatial relationships in image analysis, wavelets appear as one of the state‐of‐the‐art tools. However, image analysis is a problem‐dependent issue, and different applications might require different wavelets in order to gather the main sources of variation in the acquired images with respect to the specific task to be performed. This paper provides a methodology based on N‐way modeling for properly selecting the best wavelet choice to use or at least to provide a range of possible wavelet choices (in terms of families, filters, and decomposition levels), for each image and problem at hand. The methodology has been applied on two different data sets with exploratory and monitoring objectives. Copyright © 2015 John Wiley & Sons, Ltd.     

Simple statistically based alternatives to MAF for ToF‐SIMS spectral image analysis

The maximum autocorrelation factors technique (MAF) is becoming increasingly popular for the multivariate analysis of spectral images acquired with time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) instruments. In this article, we review the conditions under which the underlying chemical information can be separated from the large‐scale, non‐uniform noise characteristic of ToF‐SIMS data. Central to this pursuit is the ability to assess the covariance structure of the noise. Given a set of replicate images, the noise covariance matrix can be estimated in a straightforward way using standard statistical tools. Acquiring replicate images, however, is not always possible, and MAF solves a subtly different problem, namely, how to approximate the noise covariance matrix from a single image when replicates are not available. This distinction is important; the MAF approximation is not an unbiased statistical estimate of the noise covariance matrix, and it differs in a highly significant way from a true estimate for ToF‐SIMS data. Here, we draw attention to the fact that replicate measurements are made during the normal course of acquiring a ToF‐SIMS spectral image, rendering the MAF procedure unnecessary. Furthermore, in the common case that detector dead‐time effects permit no more than one ion of any specific species to be detected on a single primary ion shot, the noise covariance matrix can be estimated in a particularly simple way, which will be reported. Copyright © 2011 John Wiley & Sons, Ltd.     

Analysis of the Thermal Stability of Very Thin Surface Layers of Corrosion Inhibitors by Time-of-Flight Secondary Ion Mass Spectrometry

The powerful nature of the secondary ion mass spectrometry (SIMS) technique was explored in order to analyse very thin surface layers that were self-assembled on steel material from acidic solution. These surface layers are adsorbed corrosion inhibitors. The SIMS technique proved useful to characterise the molecular structure and homogeneity of thin surface layers in the nanometre range of specific analytes on the metallic substrate. Using SIMS, the thermal stability of these layers was further investigated and the desorption energy at a certain temperature was determined, where special attention was devoted to the method’s static limit. In order to compare, and for certain cases emphasise, the benefits gained by using SIMS in such surface analysis compared with the X-ray photoelectron spectroscopy (XPS) method, the same samples were also analysed by means of the latter. XPS is usually considered to be the most powerful analytical tool in surface analysis studies, but, as shown herein, it has certain limitations compared to SIMS. Finally, the surface topography was investigated by employing atomic force microscopy (AFM) in order to carry out a comprehensive surface analysis.

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Enhanced feature analysis using wavelets for scanning probe microscopy images of surfaces

In this work we develop wavelet theory for the analysis of surface topography images obtained by scanning probe microscopy (SPM) such as atomic force microscopy (AFM). Wavelet transformation is localized in space and frequency, which can offer an advantage for analyzing information on surface morphology and topography. Wavelet transformation is an ideal tool to detect trends, discontinuities, and short periodicities on a surface. Additionally, wavelets can be used to remove artifacts and noise from scanning microscopy images. In terms of 3-D image analysis, discrete wavelet transform can capture patterns at all relevant frequency scales, thus providing a level of image analysis that is not possible otherwise. It is also possible to use the methodology for analyzing surface structures at the molecular level. The results demonstrate superior capabilities of wavelet approach to scanning probe microscopy image analysis compared to traditional analysis techniques.     

Correlated Secondary Ion Mass Spectrometry-Laser Scanning Confocal Microscopy Imaging for Single Cell-Principles and Applications

Secondary ion mass spectrometry (SIMS) as a powerful surface analysis technique has been widely applied in semiconductor industry and geology research. Recently, with the development of instrumental technology, SIMS is attracting more and more attention in life sciences. SIMS can provide surface MS spectra, 2D/3D chemical images and depth profiling of substances simultaneously. The minimal lateral resolution of 2D SIMS imaging is 80–100 nm, and the longitudinal resolution of 3D SIMS imaging is about 1–5 nm. However, owing to lack of specific ions to render the structures of organelles, SIMS imaging for single cells still have great challenges. Optical microscopy, in particular laser scanning confocal microscopy (LSCM), has been emerged to be an indispensable technique for single cell imaging and can obtain high spatial 2D/3D imaging to visualize the structures of organelles. Thus, the combinational use of SIMS and LSCM, which takes advantages of SIMS for molecular imaging and LSCM for morphological imaging, has greatly extended the application of SIMS imaging and ensured its accuracy at single cells level, providing novel insights into better understanding of the biological events inside cells. In this review, we focus on the development and application of SIMS imaging and the correlated SIMS and LSCM imaging in the research of cell biology and drug discovery. We anticipate that the combinational use of SIMS and LSCM imaging has promising future in biomedicine and life sciences.