Exponential fitting for stripe noise reduction fromdental x-ray images

The applied mathematical field of inverse problems studies how to recover unknown function from a set of possibly incomplete and noisy observations. One example of real-life inverse problem is image destriping, which is the process of removing stripes from images. The stripe noise is a very common phenomenon in various of fields such as satellite remote sensing or in dental x-ray imaging. In this thesis we study methods to remove the stripe noise from dental x-ray images. The stripes in the images are consequence of the geometry of our measurement and the sensor. In the x-ray imaging, the x-rays are sent on certain intensity through the measurable object and then the remaining intensity is measured using the x-ray detector. The detectors used in this thesis convert the remaining x-rays directly into electrical signals, which are then measured and finally processed into an image. We notice that the gained values behave according to an exponential model and use this knowledge to transform this into a nonlinear fitting problem. We study two linearization methods and three iterative methods. We examine the performance of the correction algorithms with both simulated and real stripe images. The results of the experiments show that although some of the fitting methods give better results in the least squares sense, the exponential prior leaves some visible line artefacts. This suggests that the methods can be further improved by applying suitable regularization method. We believe that this study is a good baseline for a better correction method

Evaluating visible derivative spectroscopy by varimax-rotated, principal component analysis of aerial hyperspectral images from the western basin of Lake Erie

The Kent State University (KSU) spectral decomposition method provides information about the spectral signals present in multispectral and hyperspectral images. Pre-processing steps that enhance signal to noise ratio (SNR) by 7.37–19.04 times, enables extraction of the environmental signals captured by the National Aeronautics and Space Administration (NASA) Glenn Research Center\u27s, second generation, Hyperspectral imager (HSI2) into multiple, independent components. We have accomplished this by pre-processing of Level 1 HSI2 data to remove stripes from the scene, followed by a combination of spectral and spatial smoothing to further increase the SNR and remove non-Lambertian features, such as waves. On average, the residual stochastic noise removed from the HSI2 images by this method is 5.43 ± 1.42%. The method also enables removal of a spectrally coherent residual atmospheric bias of 4.28 ± 0.48%, ascribed to incomplete atmospheric correction. The total noise isolated from signal by the method is thu

Application of multi-window maximum cross-correlation to the mediterranean sea circulation by using MODIS data

In a previous study an improved Maximum Cross-Correlation technique, called Multi-Window Maximum Cross-Correlation (MW-MCC), was proposed, and applied to noise-free synthetic images in order to show its potential and limits in oceanographic applications. In this work, instead, the application of MW-MCC to high resolution MODIS images, and its capability to provide useful and realistic results for ocean currents, is studied. When applied to real satellite images, the MW-MCC is subject to cloud cover and image quality problems. As a consequence the number of useful MODIS images is greatly reduced. However, for every MODIS image, multiple spec-tral bands are available, and it is possible to apply the MW-MCC algorithm to the same scene as many times as the number of these bands, increasing the possibility of finding valid current vectors. Moreover, the comparison among the results from different spectral bands allows to verify both the consistency of the computed current vectors and the validity of using a spectral band as a good tracer for the ocean circulation. Due to the lack of systematic current measurements in the area considered, it has been not possible to perform an ex-tensive error analysis of the MW-MCC results, although a case study of a comparison between HF radar measurements and MW-MCC data is shown. Moreover, some comparison between numerical ocean model simulations and MW-MCC results are also shown. The coherence of the resulting circulation flow, the high number of current vectors found, the agreement among different spectral bands, and conformity with the currents measured by the HF radars or simulated by hydrodynamic models show the validity of the technique

Variational algorithms to remove stationary noise. Application to microscopy imaging.

International audienceA framework and an algorithm are presented in order to remove stationary noise from images. This algorithm is called VSNR (Variational Stationary Noise Remover). It can be interpreted both as a restoration method in a Bayesian framework and as a cartoon+texture decomposition method. In numerous denoising applications the white noise assumption fails: structured patterns (e.g. stripes) appear in the images. The model described here addresses these cases. Applications are presented with images acquired using different modalities: scan- ning electron microscope, FIB-nanotomography, and an emerging fluorescence microscopy technique called SPIM (Selective Plane Illumination Microscope)

Characterisation of a structural battery composite and its constituents

The structural battery composite is a recently successfully developed multifunctional lithium-ion battery. It is safer and capable to carry mechanical load compared to commercially available liquid electrolyte batteries. This makes it possible to apply the structural batteries to replace parts of the structural components in a system and thus reduce the weight of the whole system. The structural battery composite uses carbon fibre, an excellent lightweight material, as the anode material and uses a semi-solid structural battery electrolyte (SBE) material. The entire battery behaves as a solid material. The overall mechanical properties of the structural battery composite material are excellent due to the reinforcement of the carbon fibres and the mechanically robust SBE matrix.In this thesis, first of all, a multifunctional structural battery composite is manufactured. The structural battery composite uses the lithium storage capacity of carbon fibre for the first time and therefore, has an energy density of 24 Wh/kg and an elastic modulus of 25 GPa. Secondly, characterisation methods were developed for a number of important components in the structural battery composite. This includes precise measurements of transverse and shear moduli on micron-scale carbon fibres, the effect of lithiation on the carbon fibre anode mechanical properties, and 3D reconstruction and simulation of the SBE. For the pristine carbon fibres, focused ion beam combined with scanning electron microscopy (FIB/SEM) was used to accurately mill flat surfaces in different orientations on the carbon fibres, followed by indentation test using atomic force microscopy, and nanoindentation. The elastic hysteresis of the carbon fibres was observed in the experiments. For the first time, the moduli in the transverse and shear directions were derived in conjunction with an accurate orthotropic mechanical model. For the study of lithiation effects on the carbon fibre anode, the focus is on volume expansion and modulus changes. The volume expansion was obtained by analysis of SEM and optical micrographs. By using the protection of hydrophobic ionic liquids, the samples were successfully transferred into a vacuum environment in the SEM and subjected to transverse compression experiments. The transverse modulus of the carbon fibres is found to be doubled after lithiation. Finally, the microstructure of the SBE was reconstructed in 3D. The geodesic tortuosity of the SBE was found to be approximately 1.8. Meanwhile, the elastic modulus and ionic conductivity of the SBE were experimentally measured and simulated. In terms of elastic modulus, the results were consistent, and in terms of ionic conductivity, the simulated result overestimated the measured result

A comprehensive approach for the efficient acquisition and processing of hyperspectral images and sequence

Programa Oficial de Doctorado en Computación. 5009P01[Abstract] Despite the scientific and technological developments achieved during the last two decades in the hyperspectral field, some methodological, operational and conceptual issues have restricted the progress, promotion and popular dissemination of this technology. These shortcomings include the specialized knowledge required for the acquisition of hyperspectral images, the shortage of publicly accessible hyperspectral image repositories with reliable ground truth images or the lack of methodologies that allow for the adaptation of algorithms to particular user or application processing needs. The work presented here has the objective of contributing to the hyperspectral field with procedures for the automatic acquisition of hyperspectral scenes, including the hardware adaptation of our own imagers and the development of methods for the calibration and correction of the hyperspectral datacubes, the creation of a publicly available hyperspectral repository of well categorized and labeled images and the design and implementation of novel computational intelligence based processing techniques that solve typical issues related to the segmentation and denoising of hyperspectral images as well as sequences of them taking into account their temporal evolution.[Resumen] A pesar de los desarrollos tecnológicos y científicos logrados en el campo hiperespectral durante las dos últimas décadas, alg\mas limitaciones de tipo metodológico, operacional y conceptual han restringido el progreso, difusión y popularización de esta tecnología, entre ellas, el conocimiento especializado requerido en la adquisición de imágenes hiperespectrales, la carencia de repositorios de imágenes hiperespectrales con etiquetados fiables y de acceso público o la falta de metodologías que posibiliten la adaptación de algoritmos a usuarios o necesidades de procesamiento concretas. Este trabajo doctoral tiene el objetivo de contribuir al campo hiperespectral con procedimientos para la adquisición automática de escenas hiperespectrales, incluyendo la adaptación hardware de cámaras hiperespectrales propias y el desarrollo de métodos para la calibración y corrección de cubos de datos hiperespectrales; la creación de un repositorio hiperespectral de acceso público con imágenes categorizadas y con verdades de terreno fiables; y el diseño e implementación de técnicas de procesamiento basadas en inteligencia computacional para la resolución de problemas típicamente relacionados con las tareas de segmentación y eliminación de ruido en imágenes estáticas y secuencias de imágenes hiperespectrales teniendo en consideración su evolución temporal.[Resumo] A pesar dos desenvolvementos tecnolóxicos e científicos logrados no campo hiperespectral durante as dúas últimas décadas, algunhas lirrútacións de tipo metodolóxico¡ operacional e conceptual restrinxiron o progreso) difusión e popularización desta tecnoloxía, entre elas, o coñecemento especializado requirido na adquisición de imaxes hiperespectrales¡ a carencia de repositorios de irnaxes hiperespectrales con etiquetaxes fiables e de acceso público ou a falta de metodoloxías que posibiliten a adaptación de algoritmos a usuarios ou necesidades de procesamento concretas. Este traballo doutoral ten o obxectívo de contribuir ao campo hiperespectral con procedementos para a adquisición automática de eicenas hiperespectrais, incluíndo a adaptación hardware de cámaras hiperespectrales propias e o desenvolvemento de métodos para a calibración e corrección de cubos de datos hiperespectrais; a creación dun repositorio hiperespectral de acceso público con imaxes categorizadas e con verdades de terreo fiables; e o deseño e implementación de técnicas de procesamento baseadas en intelixencia computacional para a resolución de problemas tipicamente relacionado~ coas tarefas de segmentación e eliminación de ruído en imaxes estáticas e secuencias de imaxes hiperespectrai~ tendo en consideración a súa evolución temporal