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From simple averaging to more sophisticated registration and restoration strategies, such as super-resolution (SR), there exist different computational techniques that use a series of images of the same object to generate enhanced images where noise and other distortions have been reduced. In this work, we provide qualitative and quantitative measurements of this enhancement for high-angle annular dark-field scanning transmission electron microscopy imaging. These images are compared in two ways, qualitatively through visual inspection in real and reciprocal space, and quantitatively, through the calculation of objective measurements, such as signal-to-noise ratio and atom column roundness. Results show that these techniques improve the quality of the images. In this paper, we use an SR methodology that allows us to take advantage of the information present in the image frames and to reliably facilitate the analysis of more difficult regions of interest in experimental images, such as surfaces and interfaces. By acquiring a series of cross-sectional experimental images of magnetite (Fe3O4) thin films (111), we have generated interpolated images using averaging and SR, and reconstructed the atomic structure of the very top surface layer that consists of a full monolayer of Fe, with topmost Fe atoms in tetrahedrally coordinated sites.

During image acquisition of crystalline materials by high-resolution scanning transmission electron microscopy, the sample drift could lead to distortions and shears that hinder their quantitative analysis and characterization. In order to measure and correct this effect, several authors have proposed different methodologies making use of series of images. In this work, we introduce a methodology to determine the drift angle via Fourier analysis by using a single image based on the measurements between the angles of the second Fourier harmonics in different quadrants. Two different approaches, that are independent of the angle of acquisition of the image, are evaluated. In addition, our results demonstrate that the determination of the drift angle is more accurate by using the measurements of non-consecutive quadrants when the angle of acquisition is an odd multiple of 45°.

In this work, a new method to determine and correct the linear drift for any crystalline orientation in a single-column-resolved high-resolution scanning transmission electron microscopy (HR-STEM) image, which is based on angle measurements in the Fourier space, is presented. This proposal supposes a generalization and the improvement of a previous work that needs the presence of two symmetrical planes in the crystalline orientation to be applicable. Now, a mathematical derivation of the drift effect on two families of asymmetric planes in the reciprocal space is inferred. However, though it was not possible to find an analytical solution for all conditions, a simple formula was derived to calculate the drift effect that is exact for three specific rotation angles. Taking this into account, an iterative algorithm based on successive rotation/drift correction steps is devised to remove drift distortions in HR-STEM images. The procedure has been evaluated using a simulated micrograph of a monoclinic material in an orientation where all the reciprocal lattice vectors are different. The algorithm only needs four iterations to resolve a 15° drift angle in the image. Ministerio de Economía y Competitividad MAT2016-77491-C2-2-R Ministerio de Ciencia, Innovación y Universidades TEC2017-86102-C2-2-R

From simple averaging to more sophisticated registration and restoration strategies, such as super-resolution (SR), there exist different computational techniques that use a series of images of the same object to generate enhanced images where noise and other distortions have been reduced. In this work, we provide qualitative and quantitative measurements of this enhancement for high-angle annular dark-field scanning transmission electron microscopy imaging. These images are compared in two ways, qualitatively through visual inspection in real and reciprocal space, and quantitatively, through the calculation of objective measurements, such as signal-to-noise ratio and atom column roundness. Results show that these techniques improve the quality of the images. In this paper, we use an SR methodology that allows us to take advantage of the information present in the image frames and to reliably facilitate the analysis of more difficult regions of interest in experimental images, such as surfaces and interfaces. By acquiring a series of cross-sectional experimental images of magnetite (Fe3O4) thin films (111), we have generated interpolated images using averaging and SR, and reconstructed the atomic structure of the very top surface layer that consists of a full monolayer of Fe, with topmost Fe atoms in tetrahedrally coordinated sites.

In this paper, the concept of homogeneity is defined, from a topological perspective, in order to analyze how uniform is the material composition in 2D electron microscopy images. Topological multiresolution parameters are taken into account to obtain better results than classical techniques. Ministerio de Economía y Competitividad MTM2016-81030-P Ministerio de Economía y Competitividad TEC2012-37868-C04-02

The growth of the Internet has fuelled the availability of e-commerce marketplaces and search engines must face with a huge amount of ambiguity and inconsistencies in the data. Product matching aims at disambiguating descriptions of products belonging to different websites in order to be able to recognize identical products and to merge the content from those identical items. In this work first we evaluate some similarity measures for string matching and then, we apply a complete product matching methodology to the retail market of used cars. We use a reference or master list of items and information about a wide variety of used cars offers. The resulting linkage allows energy efficiency assignment of the model identified.

High-quality image reconstruction techniques allow the generation of high pixel density images from a set of low-resolution micrographs. In general, these techniques consist of two main steps, namely, accurate registration, and formulation of an appropriate forward image model via some restoration method. There exist a wide variety of algorithms to cope with both stages and depending on their practical applications, some methods can outperform others, since they can be sensitive to the assumed data model, noise, drift, etc. When dealing with images generated by Z-contrast scanning transmission electron microscopes, a current trend is based on non-rigid approximations in the registration stage. In our work we aimed at reaching similar accuracy but addressing the most complex calculations in the reconstruction stage, instead of in the registration stage (as the non-rigid approaches do), but using a much smaller number of images. We review some of the most significant methods and address their shortcomings when they are applied to the field of microscopy. Simulated images with known targets will be used to evaluate and compare the main approaches in terms of quality enhancement and computing time. In addition, a procedure to determine the reference image will be proposed to minimise the global drift on the series. The best registration and restoration strategies will be applied to experimental images in order to point up the enhanced capability of this high quality image reconstruction methodology in this field.

Product matching aims at disambiguating descriptions of products belonging to different websites in order to be able to recognize identical elements and to merge the content from those identical items. Most approaches face this matter applying various machine learning methods to textual product descriptions. Recently some authors are including information extracted from an image associated to a textual description of a product. Modern machine learning methods, such as content based information retrieval (CBIR) or deep learning, can be applied to this type of images since they can manage very large data sets for finding hidden structure within them, and for making accurate predictions. This information could boost the performance of the traditional textual matching but at the same time increase the computational complexity of the process. In this paper we review some CBIR and deep learning models and analyse the performance of these approaches when they are applied to images for product matching. The results obtained will help to introduce a combined classifier using textual and image information.

We demonstrate that the aberration-corrected scanning transmission electron microscope has a sufficiently small depth of field to observe depth-dependent atomic displacements in a crystal. The depth-dependent displacements associated with the Eshelby twist of dislocations in GaN normal to the foil with a screw component of the Burgers vector are directly imaged. We show that these displacements are observed as a rotation of the lattice between images taken in a focal series. From the sense of the rotation, the sign of the screw component can be determined.

Super-resolution (SR) software-based techniques aim at generating a final image by combining several noisy frames with lower resolution from the same scene. A comparative study on high-resolution high-angle annular dark field images of InAs/GaAs QDs has been carried out in order to evaluate the performance of the SR technique. The obtained SR images present enhanced resolution and higher signal-to-noise (SNR) ratio and sharpness regarding the experimental images. In addition, SR is also applied in the field of strain analysis using digital image processing applications such as geometrical phase analysis and peak pairs analysis. The precision of the strain mappings can be improved when SR methodologies are applied to experimental images.

In this paper, stress fields at the surface of the capping layer of self-assembled InAsP quantum wires grown on an InP (001) substrate have been determined from atomistic models using molecular dynamics and Stillinger-Weber potentials. To carry out these calculations, the quantum wire compositional distribution was extracted from previous works, where the As and P distributions were determined by electron energy loss spectroscopy and high-resolution aberration-corrected Z-contrast imaging. Preferential sites for the nucleation of wires on the surface of the capping layer were studied and compared with (i) previous simulations using finite element analysis to solve anisotropic elastic theory equations and (ii) experimentally measured locations of stacked wires. Preferential nucleation sites of stacked wires were determined by the maximum stress location at the MD model surface in good agreement with experimental results and those derived from finite element analysis. This indicates that MD simulations based on empirical potentials provide a suitable and flexible tool to study strain dependent atom processes.

We show in this article that it is possible to obtain elemental compositional maps and profiles with atomic-column resolution across an InxGa1?xAs multilayer structure from 5th-order aberration-corrected high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) images. The compositional profiles obtained from the analysis of HAADF-STEM images describe accurately the distribution of In in the studied multilayer in good agreement with Muraki's segregation model [Muraki, K., Fukatsu, S., Shiraki, Y. & Ito, R. (1992). Surface segregation of In atoms during molecular beam epitaxy and its influence on the energy levels in InGaAs/GaAs quantums wells. © Microscopy Society of America 2011., This work was supported by the Spanish MICINN (projects TEC2008-06756-C03-02 and CONSOLIDER INGENIO 2010 CSD2006-0019 and CSD2009-00013), the Junta de Andalucía (PAI research groups TEP-120 and TIC-145; project P08-TEP-03516 and MAT2010-15206), and CAM 2010 project S2009ESP-1503. Work at Oak Ridge National Laboratory was sponsored by the U.S. Department of Energy, Division of Materials Sciences and Engineering (M.V. and S.J.P.).

Extended abstract of a paper presented at Microscopy and Microanalysis 2012 in Phoenix, Arizona, USA, July 29 – August 2, 2012.

Extended abstract of a paper presented at Microscopy and Microanalysis 2011 in Nashville, Tennessee, USA, August 7–August 11, 2011.

In this paper we describe a methodology developed at the University of Cadiz (Spain) in the past few years for the extraction of quantitative information from electron microscopy images at the atomic level. This work is based on a coordinated and synergic activity of several research groups that have been working together over the last decade in two different and complementary fields: Materials Science and Computer Science. The aim of our joint research has been to develop innovative high-performance computing techniques and simulation methods in order to address computationally challenging problems in the analysis, modelling and simulation of materials at the atomic scale, providing significant advances with respect to existing techniques. The methodology involves several fundamental areas of research including the analysis of high resolution electron microscopy images, materials modelling, image simulation and 3D reconstruction using quantitative information from experimental images. These techniques for the analysis, modelling and simulation allow optimizing the control and functionality of devices developed using materials under study, and have been tested using data obtained from experimental samples.

High-Angle Annular Dark-Field Scanning Transmission Electron Microscopy (HAADF-STEM) in combination with strain mapping techniques provides a powerful tool for quantitative analysis of crystalline semiconductor materials. Due to the complex interaction of a focused probe and a sample in HAADF, the calculation of each pixel in a simulation process requires a complete multislice iteration, making the overall computing process a rather demanding task in time and memory. SICSTEM is a parallel software code recently developed for running on the University of Cadiz Supercomputer (3.75 Tflops) that allows the simulation of images from large nanostructures containing more than one million atoms. The software has been designed to be able to generate not only one dimensional line scans or two dimensional images, but also to perform optical sectioning in the STEM simulation process, providing an easy way to simulate 3D HAADF-STEM images. In this work we consider GaAs capped GaSb nanostructures epitaxially oriented on a GaAs substrate. A methodology has been developed by combining the through-focal series STEM imaging and image analysis to estimate shape and position of buried GaSb nanostructures.

Geometric phase and peak pairs strain mapping techniques have been applied to high angular annular dark field scanning transmission electron microscopy (HAADF-STEM) simulated images of an InAs/InP strained nanowire at different sample thicknesses. Strain values determined from HAADF-STEM images have been compared to theoretical values obtained by solving the elastic theory equation by finite element analysis, in order to analyze and assess both techniques in terms of accuracy of the predictions.