1,721,052 research outputs found
A variational framework for simultaneous motion estimation and restoration of motion-blurred video
No full textBar, Leah; Berkels, Benjamin; Rumpf, Martin; Sapiro, Guillermo. (2007). A variational framework for simultaneous motion estimation and restoration of motion-blurred video. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/5380
Denoising Methods for Multi-Dimensional Photoemission Spectroscopy
No full textThe probabilistic nature of photoemission, combined with the exploration of large multidimensional parameter spaces–including momentum, energy, time and spin polarization–necessitates time-intensive data acquisition to ensure statistical robustness. These measurements are especially important for capturing ultrafast phenomena, where pulsed light sources, such as free-electron lasers (FELs), become indispensable due to their ability to deliver high-brightness, ultrashort X-ray pulses. However, the low repetition rates of current FEL sources significantly extend acquisition times, impeding the real-time decision-making that could otherwise enhance experimental results. Hence, to optimize experimental outcomes for the valuable beamtimes, techniques that can harness the structures and correlations within the multidimensional space are necessary to accelerate data acquisition without compromising data fidelity.To address these challenges, we present an investigation into advanced denoising methodologies for multidimensional photoemission spectroscopy (MPES) data acquired with time-of-flight momentum microscopes. We focus on two key approaches: (1) employing BM3D with variance stabilization through the Anscombe transform in moderately noisy datasets and (2) leveraging a deep learning-based 3D UNET architecture, based on the Noise2Noise paradigm, excelling in low-count regimes where classical methods fail.We further establish that the photoemitted electron distributions measured with SASE FELs deviate from traditional Poissonian statistics, instead following negative binomial statistics, an outcome that has implications for denoising strategies in the MPES data.Our results demonstrate that BM3D delivers robust denoising performance for datasets with moderate average-counts (on the order of 1 ×10^−2 counts per voxel). However, in extreme low-count regimes (on the order of 1 ×10^−3 counts per voxel), where most conventional denoising techniques fail, the deep learning-based approach achieves exceptional denoising performance. Remarkably, we show that MPES datasets acquired in just 10 minutes using an FEL light source can, when processed with our deep learning model, reveal key features that remain indistinguishable even after hours of conventional measurement. The findings presented have therefore the potential to streamline data acquisition at both laboratory-scale table-top setups and large-scale facilities such as FEL FLASH. By optimizing acquisition parameters, researchers can conserve valuable beamtime or extend the scope oftheir studies to broader parameter spaces, results that hold broader implications for related experimental techniques
Automatische Detektion ungefärbter Zellen in der Hellfeld-Mikroskopie
No full textBright field microscopy is preferred over other microscopic imaging modalities whenever ease of implementation and minimization of expenditure are main concerns. This simplicity in hardware comes at the cost of image quality yielding images of low contrast. While staining can be employed to improve the contrast, it may complicate the experimental setup and cause undesired side effects on the cells. In this thesis, we tackle the problem of automatic cell detection in bright field images of unstained cells. The research was done in context of the interdisciplinary research project COSIR. COSIR aimed at developing a novel microscopic hardware having the following feature: the device can be placed in an incubator so that cells can be cultivated and observed in a controlled environment. In order to cope with design difficulties and manufacturing costs, the bright field technique was chosen for implementing the hardware. The contributions of this work are briefly outlined in the text which follows.
An automatic cell detection pipeline was developed based on supervised learning. It employs Scale Invariant Feature Transform (SIFT) keypoints, random forests, and agglomerative hierarchical clustering (AHC) in order to reliably detect cells. A keypoint classifier is first used to classify keypoints into cell and background. An intensity profile is extracted between each two nearby cell keypoints and a profile classifier is then utilized to classify the two keypoints whether they belong to the same cell (inner profile) or to different cells (cross profile). This two-classifiers approach was used in the literature. The proposed method, however, compares to the state-of-the-art as follows: 1) It yields high detection accuracy (at least 14% improvement compared to baseline bright field methods) in a fully-automatic manner with short runtime on the low-contrast bright field images. 2) Adaptation of standard features in literature from being pixel-based to adopting a keypoint-based extraction scheme: this scheme is sparse, scale-invariant, orientation-invariant, and feature parameters can be tailored in a meaningful way based on a relevant keypoint scale and orientation.
3) The pipeline is highly invariant with respect to illumination artifacts, noise, scale and orientation changes. 4) The probabilistic output of the profile classifier is used as input for an AHC step which improves detection accuracy. A novel linkage method was proposed which incorporates the information of SIFT keypoints into the linkage. This method was proved to be combinatorial, and thus, it can be computed efficiently in a recursive manner.
Due to the substantial difference in contrast and visual appearance between suspended and adherent cells, the above-mentioned pipeline attains higher accuracy in separate learning of suspended and adherent cells compared to joint learning. Separate learning refers to the situation when training and testing are done either only on suspended cells or only on adherent cells. On the other hand, joint learning refers to training the algorithm to detect cells in images which contain both suspended and adherent cells. Since these two types of cells coexist in cell cultures with shades of gray between the two terminal cases, it is of practical importance to improve joint learning accuracy. We showed that this can be achieved using two types of phase-based features: 1) physical light phase obtained by solving the transport of intensity equation, 2) monogenic local phase obtained from a low-passed axial derivative image.
In addition to the supervised cell detection discussed so far, a cell detection approach based on unsupervised learning was proposed. Technically speaking, supervised learning was utilized in this approach as well. However, instead of training the profile classifier using manually-labeled ground truth, a self-labeling algorithm was proposed with which ground truth labels can be automatically generated from cells and keypoints in the input image itself. The algorithm learns from extreme cases and applies the learned model on the intermediate ones. SIFT keypoints were successfully employed for unsupervised structure-of-interest measurements in cell images such as mean structure size and dominant curvature direction. Based on these measurements, it was possible to define the notion of extreme cases in a way which is independent from image resolution and cell type.Hellfeldmikroskopie wird immer dann anderen Mikroskopieverfahren vorgezogen, wenn großer Wert auf die Minimierung der Anschaffungskosten und die Einfachheit der Umsetzung gelegt wird. Diese Einfachheit der Hardware vermindert jedoch die Bildqualität und führt zu einem verringerten Kontrast in den erzeugten Bildern. Eine Einfärbung der Zellen kann zur Erhöhung des Kontrasts verwendet werden. Allerdings macht sie die Versuchsanordnung komplizierter und verursacht Nebenwirkungen auf die Zellen. In dieser Dissertation wurde das Problem der automatischen Detektion ungefärbter Zellen in Hellfeldmikroskopie-Bildern untersucht. Die Forschung fand im Rahmen des interdisziplinären Projekts COSIR statt. Ziel des Projekts COSIR war es, eine Mikroskop-Hardware zu entwickeln, mit der Zellkulturen innerhalb des Inkubators beobachtet werden können. Um Konstruktionschwierigkeiten zu vermeiden und Herstellungskosten gering zu halten, wurde die Hellfeldmikroskopie zur Umsetzung des COSIR-Projekts ausgewählt. Die Beiträge dieser Doktorarbeit sind im Folgenden zusammengefasst.
Basierend auf überwachtem Lernen wurde eine Pipeline zur automatischen Zelldetektion entwickelt. Sie verwendet Scale Invariant Feature Transform (SIFT), Random Forests, und die agglomerative hierarchische Clusteranalyse (AHC), um Zellen zuverlässig zu detektieren. Als erster Schritt wurde ein Keypoint-Klassifikator zur Unterscheidung zwischen Zell- und Hintergrund-Keypoints eingesetzt. Danach wurde ein Intensitätsprofil zwischen je zwei nebeneinanderliegenden Zell-Keypoints extrahiert. Ein Profil-Klassifikator wurde danach verwendet, damit die Profile entweder als inner (in derselben Zelle) oder cross (zwischen zwei Zellen) klassifiziert werden. Dieser Zwei-Klassifikatoren-Ansatz wurde bereits in der Literatur verwendet. Im Gegensatz zu anderen State-of-the-Art Algorithmen trägt der vorgeschlagene Ansatz das Folgende bei: 1) Die Zelldetektion ist vollautomatisch, arbeitet mit hoher Genauigkeit (mindestens 14% besser als Baseline Hellfeld-Ansätze) und in kurzer Zeit auf kontrastarmen Hellfeldbildern. 2) Pixelbasierte Standardmerkmale aus der Literatur wurden basierend auf SIFT-Keypoints angepasst. Dieser Ansatz ist dünnbesetzt, skaleninvariant, rotationsinvariant, und die Parameter der Merkmale können basierend auf der relevanten Vergrößerung und der relevanten Orientierung sinnvoll angepasst werden. 3) Die vorgeschlagene Pipeline ist weitgehend invariant gegenüber Beleuchtungsartefakten, Rauschen, und Änderungen der Vergrößerung oder der Orientierung. 4) Die probabilistische Ausgabe des Profil-Klassifikators wird als Eingabe eines AHC-Verfahrens genutzt, was die Genauigkeit der Detektion verbessert. Ein neues Linkage-Verfahren wurde dargestellt, das die Informationen der SIFT-Keypoints ins Linkage-Verfahren einbezieht. Es wurde bewiesen, dass dieses Verfahren kombinatorisch ist. Daher kann es effizient in rekursiver Weise berechnet werden.
Wegen des erheblichen Unterschieds zwischen adhärenten Zellen und Suspensionszellen sowohl im Kontrast als auch im Erscheinungsbild, liefert die oben aufgeführte Pipeline eine niedrigere Detektionsgenauigkeit bei gemeinsamem Lernen im Vergleich zum separaten Lernen. Separates Lernen bezieht sich auf die Situation, in der Training und Testen entweder nur auf adhärente Zellen oder nur auf Suspensionszellen angewandt werden. Auf der anderen Seite bezieht sich das gemeinsame Lernen auf die Situation, in der adhärente Zellen und Suspensionszellen zusammen in den Trainingsbildern enthalten sind. Da diese zwei Zelltypen in Zellkulturen koexistieren, ist die Verbesserung des gemeinsamen Lernens wichtig für die Praxis. Wir haben
gezeigt, dass dieses Ziel mit zwei Typen phasenbasierter Merkmale erreicht werden kann: 1) Die Phase des physikalischen Lichts, die man durch das Lösen der Transport of Intensity Equation erhält. 2) Die monogene lokale Phase, die basierend auf einer tiefpassgefilterten axialen Ableitung berechnet werden kann.
Zusätzlich zur bisher diskutierten überwachten Zelldetektion, wurde ein Ansatz zur unüberwachten Zelldetektion vorgeschlagen. Technisch gesehen, wurde auch hier überwachtes Lernen benutzt. Statt des Trainings des Profil-Klassifikators mit manuell gelabelten Ground-Truth-Daten, wurde ein Self-Labeling Algorithmus vorgeschlagen, mit dem Labels basierend auf Zellen und Keypoints im Bild automatisch erzeugt werden können. Der Algorithmus lernt aus extremen Fällen und wendet das gelernte Model auf die dazwischenliegenden Fälle an. SIFT-Keypoints wurden erfolgreich für Ermittlung der relevanten Strukturen (z.B. die mittlere Strukturgröße und die dominante Krümmungsrichtung) eingesetzt. Anhand dieser ermittelten Werte war es möglich, ein Konzept für die extremen Fälle zu definieren, das unabhängig von dem Zelltyp oder der Bildauflösung ist
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Joint exit wave reconstruction and multimodal registration of transmission electron microscopy image series
No full textImages generated with a transmission electron microscope (TEM) can reveal information up to the scale of individual atoms. However, the information contained in a TEM image is blurred by aberrations and the partial coherence of the electron beam. Furthermore, the images correspond to the squared amplitude of the image plane electron wave and are thus missing valuable information about the phase. Exit wave reconstruction attempts to solve these problems by reconstructing the electron wave at the exit plane of the specimen, the so-called exit wave, from a series of images recorded with varying focus of the objective lens. This introduces the additional problem of aligning the image series, which is crucial for a successful reconstruction of the exit wave. One possible approach to reconstructing the exit wave involves the minimization of a least squares functional, which is implemented by the well-known MIMAP and MAL algorithms. The MIMAP and MAL algorithms solve the registration problem by alternatingly optimizing the exit wave and the registration. In this thesis, a novel objective functional for the joint optimization of the exit wave and the registration is proposed. The properties of the forward model of TEM image simulation, which is given by a weighted autocorrelation of the exit wave, are investigated on the basis of the weighted cross-correlation and the novel notion of -separable weights. The most important weight functions (commonly called transmission cross-coefficients, TCCs) for TEM image simulation are analyzed and integrated into the present framework. The results regarding the forward model are then used for the analysis of the inverse problem. It is shown that the data term of is not coercive for -separable TCCs, which in particular implies that the MAL functional is not coercive. One of the main results is the existence of minimizers of the objective functional , which is shown with the direct method. Additionally, it is shown that the objective functional is not convex in general. These results are complemented by a numerical analysis, which includes the discretization of the objective functional and the treatment of several problems regarding the numerical minimization of . A novel preconditioner for the exit wave is proposed, showing a reduction of the number of iterations for a given residual energy. The least squares sum in the data term of the objective functional is usually calculated by summing the squared differences of the simulated and experimental images over the same domain for each image. A novel method for the dynamic adjustment of these domains based on the current estimate for the registration is proposed, which allows to use the full image data for the reconstruction while at the same time avoiding the need for a continuation of the images. Numerical experiments are presented that evaluate the utility of the preconditioner and compare the alternating optimization approach with the joint optimization of the exit wave and the registration. Finally, a numerical experiment shows the result of reconstructing the exit wave for a real image series
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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