Tesis sobre el tema "Super learning"
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Lindberg, Magnus. "An Imitation-Learning based Agentplaying Super Mario". Thesis, Blekinge Tekniska Högskola, Institutionen för kreativa teknologier, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4529.
Kumar, Sanjeev. "Priors and learning based methods for super-resolution". Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/ucsd/fullcit?p3397852.
Title from first page of PDF file (viewed April 14, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 96-102).
Pickup, Lyndsey C. "Machine learning in multi-frame image super-resolution". Thesis, University of Oxford, 2007. http://ora.ox.ac.uk/objects/uuid:88c6968f-1e62-4d89-bd70-604bf1f41007.
Ouyang, Wei. "Deep Learning for Advanced Microscopy". Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCC174/document.
Background: Microscopy plays an important role in biology since several centuries, but its resolution has long been limited to ~250nm due to diffraction, leaving many important biological structures (e.g. viruses, vesicles, nuclear pores, synapses) unresolved. Over the last decade, several super-resolution methods have been developed that break this limit. Among the most powerful and popular super-resolution techniques are those based on single molecular localization (single molecule localization microscopy, or SMLM) such as PALM and STORM. By precisely localizing positions of isolated fluorescent molecules in thousands or more sequentially acquired diffraction limited images, SMLM can achieve resolutions of 20-50 nm or better. However, SMLM is inherently slow due to the necessity to accumulate enough localizations to achieve high resolution sampling of the fluorescent structures. The drawback in acquisition speed (typically ~30 minutes per super-resolution image) makes it difficult to use SMLM in high-throughput and live cell imaging. Many methods have been proposed to address this issue, mostly by improving the localization algorithms to localize overlapping spots, but most of them compromise spatial resolution and cause artifacts.Methods and results: In this work, we applied deep learning based image-to-image translation framework for improving imaging speed and quality by restoring information from rapidly acquired low quality SMLM images. By utilizing recent advances in deep learning including the U-net and Generative Adversarial Networks, we developed our method Artificial Neural Network Accelerated PALM (ANNA-PALM) which is capable of learning structural information from training images and using the trained model to accelerate SMLM imaging by tens to hundreds folds. With experimentally acquired images of different cellular structures (microtubules, nuclear pores and mitochondria), we demonstrated that deep learning can efficiently capture the structural information from less than 10 training samples and reconstruct high quality super-resolution images from sparse, noisy SMLM images obtained with much shorter acquisitions than usual for SMLM. We also showed that ANNA-PALM is robust to possible variations between training and testing conditions, due either to changes in the biological structure or to changes in imaging parameters. Furthermore, we take advantage of the acceleration provided by ANNA-PALM to perform high throughput experiments, showing acquisition of ~1000 cells at high resolution in ~3 hours. Additionally, we designed a tool to estimate and reduce possible artifacts is designed by measuring the consistency between the reconstructed image and the experimental wide-field image. Our method enables faster and gentler imaging which can be applied to high-throughput, and provides a novel avenue towards live cell high resolution imaging. Deep learning methods rely on training data and their performance can be improved even further with more training data. One cheap way to obtain more training data is through data sharing within the microscopy community. However, it often difficult to exchange or share localization microscopy data, because localization tables alone are typically several gigabytes in size, and there is no dedicated platform for localization microscopy data which provide features such as rendering, visualization and filtering. To address these issues, we developed a file format that can losslessly compress localization tables into smaller files, alongside with a web platform called ShareLoc (https://shareloc.xyz) that allows to easily visualize and share 2D or 3D SMLM data. We believe that this platform can greatly improve the performance of deep learning models, accelerate tool development, facilitate data re-analysis and further promote reproducible research and open science
Yelibi, Lionel. "Introduction to fast Super-Paramagnetic Clustering". Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31332.
Bégin, Isabelle. "Camera-independent learning and image quality assessment for super-resolution". Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102957.
Learning-based methods have been successfully applied to the single frame super-resolution problem in the past. However, sensor characteristics such as the Point Spread Function (PSF) must often be known. In this thesis, a learning-based approach is adapted to work without the knowledge of the PSF thus making the framework camera-independent. However, the goal is not only to super-resolve an image under this limitation, but also to provide an estimation of the best PSF, consisting of a theoretical model with one unknown parameter.
In particular, two extensions of a method performing belief propagation on a Markov Random Field are presented. The first method finds the best PSF parameter by performing a search for the minimum mean distance between training examples and patches from the input image. In the second method, the best PSF parameter and the super-resolution result are found simultaneously by providing a range of possible PSF parameters from which the super-resolution algorithm will choose from. For both methods, a first estimate is obtained through blind deconvolution and an uncertainty is calculated in order to restrict the search.
Both camera-independent adaptations are compared and analyzed in various experiments, and a set of key parameters are varied to determine their effect on both the super-resolution and the PSF parameter recovery results. The use of quality measures is thus essential to quantify the improvements obtained from the algorithms. A set of measures is chosen that represents different aspects of image quality: the signal fidelity, the perceptual quality and the localization and scale of the edges.
Results indicate that both methods improve similarity to the ground truth and can in general refine the initial PSF parameter estimate towards the true value. Furthermore, the similarity measure results show that the chosen learning-based framework consistently improves a measure designed for perceptual quality.
Jain, Vinit. "Deep Learning based Video Super- Resolution in Computer Generated Graphics". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292687.
Superupplösning är ett allmänt studerat problem inom datorsyn, där syftet är att öka upplösningen på eller superupplösningsbilddata. I Video Super- Resolution kräver upprätthållande av tidsmässig koherens för på varandra följande videobilder sammanslagning av information från flera bilder för att superlösa en bildruta. Nuvarande djupinlärningsmetoder utför superupplösning i video, men de flesta av dem fokuserar på att arbeta med naturliga datamängder. I denna avhandling använder vi ett återkommande bakprojektionsnätverk för att arbeta med en datamängd av datorgenererad grafik, med exempelvis applikationer inklusive upsampling av film med låg upplösning för spelindustrin. Datauppsättningen kommer från en mängd olika spelinnehåll, återgivna i (3840 x 2160) upplösning. Målet med nätverket är att producera en uppskalad version av en ram med låg upplösning genom att lära sig en ingångskombination av en lågupplösningsram, en sekvens av intilliggande ramar och det optiska flödet mellan varje intilliggande ram och referensramen. Under grundinställningen tränar vi modellen för att utföra 2x uppsampling från (1920 x 1080) till (3840 x 2160) upplösning. Jämfört med den bicubiska interpoleringsmetoden uppnådde vår modell bättre resultat med en marginal på 2 dB för Peak Signal-to-Noise Ratio (PSNR), 0,015 för Structural Similarity Index Measure (SSIM) och 9.3 för Video Multimethod Assessment Fusion (VMAF) mätvärde. Dessutom demonstrerar vi vidare känsligheten i neuronal nätverk för förändringar i bildkomprimeringskvaliteten och ineffektiviteten hos distorsionsmätvärden för att fånga de perceptuella detaljerna exakt.
Donnot, Benjamin. "Deep learning methods for predicting flows in power grids : novel architectures and algorithms". Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS060/document.
This thesis addresses problems of security in the French grid operated by RTE, the French ``Transmission System Operator'' (TSO). Progress in sustainable energy, electricity market efficiency, or novel consumption patterns push TSO's to operate the grid closer to its security limits. To this end, it is essential to make the grid ``smarter''. To tackle this issue, this work explores the benefits of artificial neural networks. We propose novel deep learning algorithms and architectures to assist the decisions of human operators (TSO dispatchers) that we called “guided dropout”. This allows the predictions on power flows following of a grid willful or accidental modification. This is tackled by separating the different inputs: continuous data (productions and consumptions) are introduced in a standard way, via a neural network input layer while discrete data (grid topologies) are encoded directly in the neural network architecture. This architecture is dynamically modified based on the power grid topology by switching on or off the activation of hidden units. The main advantage of this technique lies in its ability to predict the flows even for previously unseen grid topologies. The "guided dropout" achieves a high accuracy (up to 99% of precision for flow predictions) with a 300 times speedup compared to physical grid simulators based on Kirchoff's laws even for unseen contingencies, without detailed knowledge of the grid structure. We also showed that guided dropout can be used to rank contingencies that might occur in the order of severity. In this application, we demonstrated that our algorithm obtains the same risk as currently implemented policies while requiring only 2% of today's computational budget. The ranking remains relevant even handling grid cases never seen before, and can be used to have an overall estimation of the global security of the power grid
Kim, Max. "Improving Knee Cartilage Segmentation using Deep Learning-based Super-Resolution Methods". Thesis, KTH, Medicinteknik och hälsosystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297900.
Segmentering av knäbrosket är ett viktigt steg för planering inför operationer och tillverkning av patientspecifika proteser. Idag segmenterar man knäbrosk med hjälp av MR-bilder tagna med en 3D-sekvens som både tidskrävande och rörelsekänsligt, vilket kan vara obehagligt för patienten. I samband med 3D-bildtagningar brukar även thick slice 2D-sekvenser tas för diagnostiska skäl, däremot är de inte anpassade för segmentering på grund av för tjocka skivor. På senare tid har djupinlärningsbaserade superupplösningsmetoder uppbyggda av så kallade feed-forwardmodeller visat sig vara väldigt framgångsrikt när det applicerats på verkliga- och medicinska bilder. Syftet med den här rapporten är att testa hur väl superupplösta thick slice 2D-sekvensbildtagningar fungerar för segmentering av ledbrosket i knät. De undersökta tillvägagångssätten är superupplösning av enkel- och flerkontrastbilder, där kontrasten är antingen baserade på 2D-sekvensen, 3D-sekvensen eller både och. Resultaten påvisar en liten förbättring av segmenteringnoggrannhet vid segmentering av enkelkontrastbilderna över baslinjen linjär interpolering. Däremot var det inte någon märkvärdig förbättring i superupplösning av flerkontrastbilderna. Även om superupplösning av flerkontrastmetoden inte gav någon märkbar förbättring segmenteringsresultaten så finns det fortfarande outforskade områden som inte tagits upp i det här arbetet som potentiellt skulle kunna utforskas i framtida arbeten.
Ceccarelli, Mattia. "Optimization and applications of deep learning algorithms for super-resolution in MRI". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21694/.
Bevilacqua, Marco. "Algorithms for super-resolution of images and videos based on learning methods". Phd thesis, Université Rennes 1, 2014. http://tel.archives-ouvertes.fr/tel-01064396.
Firoiu, Vlad. "Beating the world's best at Super Smash Bros. with deep reinforcement learning". Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/108984.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 29).
There has been a recent explosion in the capabilities of game-playing artificial intelligence. Many classes of RL tasks, from Atari games to motor control to board games, are now solvable by fairly generic algorithms, based on deep learning, that learn to play from experience with often minimal knowledge of the specific domain of interest. In this work, we will investigate the performance of these methods on Super Smash Bros. Melee (SSBM), a popular multiplayer fighting game. The SSBM environment has complex dynamics and partial observability, making it challenging for man and machine alike. The multiplayer aspect poses an additional challenge, as the vast majority of recent advances in RL have focused on single-agent environments. Nonetheless, we will show that it is possible to train agents that are competitive against and even surpass human professionals, a new result for the video game setting..
by Vlad Firoiu.
S.M.
Vassilo, Kyle. "Single Image Super Resolution with Infrared Imagery and Multi-Step Reinforcement Learning". University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1606146042238906.
Trinh, Dinh Hoan. "Denoising and super-resolution for medical images by example-based learning approach". Paris 13, 2013. http://scbd-sto.univ-paris13.fr/secure/edgalilee_th_2013_trinh.pdf.
Ribeiro, Eduardo Ferreira. "Exploring Transfer Learning via Convolutional Neural Networks for Image Classification and Super-Resolution". Universidade de Salzburg, 2018. http://hdl.handle.net/11612/1009.
Diese Arbeit pr¨asentiert meine Forschung hinsichtlich der Verwendung von ”Transfer-Learning” (TL) in Kombination mit Convolutional Neural Networks (CNNs), um dadurch die Klassifikation von Dickdarmpolypen und die Qualit¨at von Iris Bildern (”Iris-Super-Resolution”) zu verbessern. Herk¨ommlicherweise verwenden Verfahren des maschinellen Lernens den gleichen Merkmalsraum und die gleiche Verteilung zum Trainieren und Testen der abgewendeten Methoden. Mehrere Probleme k¨onnen bei diesem Ansatz jedoch auftreten. Zum Beispiel ist es m¨ oglich, dass die Anzahl der zu trainierenden Daten (insbesondere in einem ”supervised training” Szenario) begrenzt ist. Im Speziellen im medizinischen Anwendungsfall ist man regelm¨aßig mit dem angesprochenen Problem konfrontiert, da die Zusammenstellung einer Datenbank, welche ¨ uber eine geeignete Anzahl an verwendbaren Daten verf ¨ ugt, entweder sehr kostspielig ist und/oder sich als ¨ uber die Maßen zeitaufw¨andig herausstellt. Ein anderes Problem betrifft die Verteilung von Strukturmerkmalen in einer Bilddatenbank, die zu groß sein kann, wie es im Fall der Verwendung von Texturmustern der menschlichen Iris auftritt. Dies kann zu dem Umstand f ¨ uhren, dass eine einzelne und sehr spezifische Trainingsdatenbank m¨oglicherweise nicht ausreichend verallgemeinert wird, um sie auf die gesamte betrachtete Dom¨ane anzuwenden. In dieser Arbeit wird die Verwendung von TL auf diverse Texturen untersucht, um die zuvor angesprochenen Probleme f ¨ ur zwei Anwendungen zu ¨ uberwinden: in der Klassifikation von Dickdarmpolypen und in Iris Super-Resolution. Die Hauptursache f ¨ ur Todesf¨alle im Zusammenhang mit dem Darmtrakt ist die Entwicklung von Krebszellen (Polypen) in vielen unterschiedlichen Auspr¨agungen. Eine Fr ¨uherkennung kann das Mortalit¨atsrisiko bei Patienten verringern, wenn sich der Krebs noch in einem fr ¨uhen Stadium befindet. Genauer gesagt, Dickdarmpolypen (gutartige Tumore oder Wucherungen, die an der inneren Dickdarmoberfl¨ache entstehen) haben ein hohes Vorkommen und sind bekanntermaßen Vorl¨aufer von Darmkrebsentwicklung. Mehrere Studien haben gezeigt, dass die automatische Erkennung und Klassifizierung von Bildregionen, die Polypen innerhalb des Dickdarms m¨oglicherweise enthalten, verwendet werden k¨onnen, um Spezialisten zu helfen, die Fehlerrate bei Polypen zu verringern. Die Klassifizierung kann sich jedoch aufgrund mehrerer Faktoren als eine schwierige Aufgabe herausstellen. ZumBeispiel kann das Fehlen oder ein U¨ bermaß an Beleuchtung zu starken Problemen hinsichtlich der Kontrastinformation der Bilder f ¨ uhren, wohingegen Unsch¨arfe aufgrund von Bewegung/Wassereinspritzung die Qualit¨at des Bildmaterials ebenfalls verschlechtert. Daten, welche ein unterschiedlich starkes Auftreten von Polypen repr¨asentieren, bieten auch dieM¨oglichkeit zu einer Reduktion der Klassifizierungsgenauigkeit. Weiters ist es sehr schwierig, einen robusten und vor allem globalen Feature-Extraktor zu finden, der all die notwendigen Pit-Pattern-Strukturen in einem einzigen Vektor zusammenfasst und darstellt. Um mit diesen Problemen ad¨aquat umzugehen, kann die Anwendung von CNNs eine gute Alternative bieten. Eines der Ziele dieser Arbeit ist es, die Wirksamkeit von CNNs, die von Grund auf f ¨ ur die Klassifikation von Dickdarmpolypen konstruiert wurden, zu zeigen. Des Weiteren soll die Anwendung von TL unter der Verwendung vorgefertigter CNNs f ¨ ur die Klassifikation von Dickdarmpolypen untersucht werden. Hierbei wird zus¨atzliche Information von nichtmedizinischen Bildern hinzugezogen und mit den verwendeten medizinischen Daten verbunden: Information wird also transferiert - TL entsteht. Auch in diesem Fall projiziert das CNN iii die Zieldatenbank (die Polypenbilder) in einen vorher trainierten Vektorraum, in dem die zu separierenden Klassen dann eher trennbar sind, daWissen aus den nicht-medizinischen Bildern einfließt. Der zweite Teil dieser Arbeit widmet sich dem TL hinsichtlich der Verbesserung der Bildqualit¨at von Iris Bilder - ”Iris- Super-Resolution”. Das Hauptziel von Super-Resolution (SR) ist es, aus einem oder mehreren Bildern gleichzeitig ein Bild mit einer h¨oheren Aufl¨osung (mit mehr Pixeln) zu erzeugen, welches dadurch zu einem detaillierteren und somit realistischeren Bild wird, wobei der visuelle Bildinhalt unver¨andert bleibt. Gegenw¨artig fordern die meisten Iris- Erkennungssysteme, dass der Benutzer seine Iris f ¨ ur den Sensor in geringer Entfernung pr¨asentiert. Jedoch ist es ein Anliegen der Industrie die bisher notwendigen Bedingungen - kurzer Abstand zwischen Sensor und Iris, sowie Verwendung von sehr teuren hochqualitativen Sensoren - zu ver¨andern. Diese Ver¨anderung betrifft einerseits die Verwendung von billigeren Sensoren und andererseits die Vergr¨oßerung des Abstandes zwischen Iris und Sensor. Beide Anpassungen f ¨ uhren zu Reduktion der Bildqualit¨at, was sich direkt auf die Erkennungsgenauigkeit der aktuell verwendeten Iris- erkennungssysteme auswirkt. In dieser Arbeit zeigen wir, dass die Verwendung von CNNs und TL f ¨ ur die ”Single Image Super-Resolution”, die bei der Iriserkennung angewendet wird, eine Alternative f ¨ ur die Iriserkennung von Bildern mit niedriger Aufl¨osung sein kann. Zu diesem Zweck untersuchen wir, ob die Art der Bilder sowie das Muster der Iris das CNN-TL beeinflusst und folglich die Ergebnisse im Erkennungsprozess ver¨andern kann.
Mathari, Bakthavatsalam Pagalavan. "Hardware Acceleration of a Neighborhood Dependent Component Feature Learning (NDCFL) Super-Resolution Algorithm". University of Dayton / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1366034621.
BORDONE, MOLINI ANDREA. "Deep learning for inverse problems in remote sensing: super-resolution and SAR despeckling". Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2903492.
Polini, Elena. "Super Resolution di immagini con reti neurali convoluzionali". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18502/.
Sargent, Garrett Craig. "Single-Image Super-Resolution via Regularized Extreme Learning Regression for Imagery from Microgrid Polarimeters". University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1492782713231794.
Sha, Feng. "Advanced Evolutionary Computation and Deep Learning for Real-Time Video Target Tracking and Image Processing". Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/21992.
Rezio, Ana Carolina Correia 1986. "Super-resolução de imagens baseada em aprendizado utilizando descritores de características". [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275719.
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
Made available in DSpace on 2018-08-19T11:40:39Z (GMT). No. of bitstreams: 1 Rezio_AnaCarolinaCorreia_M.pdf: 2407538 bytes, checksum: cbf48e9214024f2478edcaa47e002852 (MD5) Previous issue date: 2011
Resumo: Atualmente, há uma crescente demanda por imagens de alta resolução em diversos domínios de conhecimento, como sensoriamento remoto, medicina, automação industrial, microscopia, entre outros. Imagens de alta resolução fornecem detalhes que são importantes para as tarefas de análise e visualização dos dados presentes nas imagens. Entretanto, devido ainda ao custo elevado dos sensores de alta precisão e às limitações existentes para redução do tamanho dos pixels das imagens encontradas no próprio sensor, as imagens de alta resolução têm sido adquiridas a partir de métodos de super-resolução. Este trabalho propõe um método para super-resolver uma imagem ou uma sequência de imagens a partir da compensação residual aprendida pelas características extraídas na imagem residual e no conjunto de treinamento. Resultados experimentais mostram que, na maioria casos, o método proposto provê menores erros quando comparado com outras abordagens do estado da arte. Medidas quantitativas e qualitativas são utilizadas na comparação dos resultados obtidos com as técnicas de super-resolução consideradas nos experimentos
Abstract: There is currently a growing demand for high-resolution images in several domains of knowledge, such as remote sensing, medicine, industrial automation, microscopy, among others. High resolution images provide details that are important to tasks of analysis and visualization of data present in the images. However, due to the cost of high precision sensors and the limitations that exist for reducing the size of the image pixels in the sensor itself, high-resolution images have been acquired from super-resolution methods This work proposes a method for super-resolving an image or a sequence of images from the compensation residual learned by the features extracted in the residual image and the training set. The results are compared with some methods available in the literature. Quantitative and qualitative measures are used to compare the results obtained with the super-resolution techniques considered in the experiments
Mestrado
Ciência da Computação
Mestre em Ciência da Computação
Gawande, Saurabh. "Generative adversarial networks for single image super resolution in microscopy images". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230188.
Image Super-resolution är ett allmänt studerad problem i datasyn, där målet är att konvertera en lågupplösningsbild till en högupplöst bild. Konventionella metoder för att uppnå superupplösning som image priors, interpolation, sparse coding behöver mycket föroch efterbehandling och optimering.Nyligen djupa inlärningsmetoder som convolutional neurala nätverk och generativa adversariella nätverk är användas för att utföra superupplösning med resultat som är konkurrenskraftiga mot toppmoderna teknik, men ingen av dem har använts på mikroskopibilder. I denna avhandling, ett generativ kontradiktorisktsnätverk, mSRGAN, är föreslås för superupplösning med en perceptuell förlustfunktion bestående av en motsatt förlust, medelkvadratfel och innehållförlust.Mål med vår implementering är att lära oss ett slut på att slut kartläggning mellan bilder med låg / hög upplösning och optimera den uppskalade bilden för kvantitativa metriks såväl som perceptuell kvalitet. Vi jämför sedan våra resultat med de nuvarande toppmoderna metoderna i superupplösning, och uppträdande ett bevis på konceptsegmenteringsstudie för att visa att superlösa bilder kan användas som ett effektivt förbehandling steg före segmentering och validera fynden statistiskt.
Sebastiani, Andrea. "Deep Plug-and-Play Gradient Method for Super-Resolution". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20619/.
Zandavi, Seid Miad. "Indoor Autonomous Flight Using Deep Learning-Based Image Understanding Techniques". Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/22893.
Nilsson, Erik. "Super-Resolution for Fast Multi-Contrast Magnetic Resonance Imaging". Thesis, Umeå universitet, Institutionen för fysik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160808.
Castillo, Araújo Victor. "Ensembles of Single Image Super-Resolution Generative Adversarial Networks". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290945.
Generative Adversarial Networks (GANs) har använts för att uppnå state-of-the- art resultat för grundläggande bildanalys uppgifter, som generering av högupplösta bilder från bilder med låg upplösning, men de är notoriskt svåra att träna på grund av instabiliteten relaterad till det konkurrerande minimax-ramverket. Dessutom kan traditionella mekanismer för att generera ensembler inte tillämpas effektivt med dessa typer av nätverk på grund av de resurser de behöver vid inferenstid och deras arkitekturs komplexitet. I det här projektet har en alternativ metod för att samla enskilda, mer stabila och modeller som är lättare att träna genom interpolation i parameterrymden visat sig ge bättre perceptuella resultat än de ursprungliga enskilda modellerna och denna metod kan användas som ett ramverk för att träna GAN med konkurrenskraftig perceptuell prestanda jämfört med toppmodern teknik.
Pham, Chi-Hieu. "Apprentisage profond pour la super-résolution et la segmentation d'images médicales". Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2018. http://www.theses.fr/2018IMTA0124/document.
In this thesis, our motivation is dedicated to studying the behaviors of different image representations and developing a method for super-resolution, cross-modal synthesis and segmentation of medical imaging. Super-Resolution aims to enhance the image resolution using single or multiple data acquisitions. In this work, we focus on single image super-resolution (SR) that estimates the high-resolution (HR) image from one corresponding low-resolution (LR) image. Increasing image resolution through SR is a key to more accurate understanding of the anatomy. The applications of super-resolution have been shown that applying super-resolution techniques leads to more accurate segmentation maps. Sometimes, certain tissue contrasts may not be acquired during the imaging session because of time-consuming, expensive costor lacking of devices. One possible solution is to use medical image cross-modal synthesis methods to generate the missing subject-specific scans in the desired target domain from the given source image domain. The objective of synthetic images is to improve other automatic medical image processing steps such as segmentation, super-resolution or registration. In this thesis, convolutional neural networks are applied to super-resolution and cross-modal synthesis in the context of supervised learning. In addition, an attempt to apply generative adversarial networks for unpaired cross-modal synthesis brain MRI is described. Results demonstrate the potential of deep learning methods with respect to practical medical applications
Peyrard, Clément. "Single image super-resolution based on neural networks for text and face recognition". Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEI083/document.
This thesis is focussed on super-resolution (SR) methods for improving automatic recognition system (Optical Character Recognition, face recognition) in realistic contexts. SR methods allow to generate high resolution images from low resolution ones. Unlike upsampling methods such as interpolation, they restore spatial high frequencies and compensate artefacts such as blur or jaggy edges. In particular, example-based approaches learn and model the relationship between low and high resolution spaces via pairs of low and high resolution images. Artificial Neural Networks are among the most efficient systems to address this problem. This work demonstrate the interest of SR methods based on neural networks for improved automatic recognition systems. By adapting the data, it is possible to train such Machine Learning algorithms to produce high-resolution images. Convolutional Neural Networks are especially efficient as they are trained to simultaneously extract relevant non-linear features while learning the mapping between low and high resolution spaces. On document text images, the proposed method improves OCR accuracy by +7.85 points compared with simple interpolation. The creation of an annotated image dataset and the organisation of an international competition (ICDAR2015) highlighted the interest and the relevance of such approaches. Moreover, if a priori knowledge is available, it can be used by a suitable network architecture. For facial images, face features are critical for automatic recognition. A two step method is proposed in which image resolution is first improved, followed by specialised models that focus on the essential features. An off-the-shelf face verification system has its performance improved from +6.91 up to +8.15 points. Finally, to address the variability of real-world low-resolution images, deep neural networks allow to absorb the diversity of the blurring kernels that characterise the low-resolution images. With a single model, high-resolution images are produced with natural image statistics, without any knowledge of the actual observation model of the low-resolution image
Kabir, Md Faisal. "Extracting Useful Information and Building Predictive Models from Medical and Health-Care Data Using Machine Learning Techniques". Diss., North Dakota State University, 2020. https://hdl.handle.net/10365/31924.
Bai, Jiachuan. "Deep learning augmented single molecule localization microscopy reconstruction : enhancing robustness and moving towards live cells". Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS337.pdf.
While microscopy has been a central technique for cell biology since centuries, it has long been limited by diffraction to a resolution of ~200-300 nm. As a consequence, many molecular structures, such as viruses, nuclear pores, or microtubules were left unresolved. Single-molecule localization microscopy (SMLM) offers a high spatial resolution (e.g., 20 nm or better), allowing to resolve biological structures at or near the molecular scale. However, SMLM acquisition necessitates acquiring many thousands of low-resolution frames, mostly limiting its applications to fixed cells or to structures undergoing slow dynamics. To overcome this limitation, Ouyang et al. (2018) developed a deep learning-based approach called ANNA-PALM that can reconstruct a super-resolution image from much fewer low-resolution frames. However, the original ANNA-PALM method faced several limitations. First, ANNA-PALM had only been trained and tested on images from our laboratory. Second, the method exhibits artifacts when applied to images obtained using different protocols or experimental conditions than the training data. Third, ANNA-PALM had only been demonstrated on fixed cells. The objectives of my Ph.D. thesis are to address these limitations by 1) improving the robustness of ANNA-PALM reconstructions when applied to data obtained from distinct laboratories and 2) extending ANNA-PALM to reconstruct super-resolved time-lapse image sequences for dynamic biologicalstructures in live cells. 1. Improving the robustness of ANNA PALM: an obvious approach to improve robustness is to retrain the model using a larger and more varied data set. However, SMLM datasets are not usually publicly accessible. To address this, our lab developed ShareLoc, an online platform (shareloc.xyz) that allows the gathering and reuse of SMLM datasets acquired by the microscopy community. I first validated the platform's functionalities, curated SMLM data, implemented a ShareLoc ontology, and wrote relevant documentation. Next, I took advantage of ShareLoc data to retrain ANNA-PALM on larger and more diverse images and quantitatively evaluated the image reconstruction quality compared to the original model. I demonstrated that the robustness and reconstruction quality of ANNA-PALM significantly improved, notably when applied to images of microtubules taken under biological perturbation conditions never seen by the model during training. 2. Extending ANNA-PALM to reconstruct super-resolution movies of moving structures in live cells: achieving high-quality super-resolution reconstructions of structural dynamics is challenging. To avoid motion blur, each frame of the reconstructed movie is defined from localizations in only a small number of consecutive low-resolution frames. This leads to a strong under-sampling of the structures by single molecule localization events and does not enable super-resolution. Although ANNA-PALM can reconstruct high-quality super-resolved images from under-sampled localization data, training ANNA-PALM for live cells is more difficult, because a clear ground truth is lacking. The absence of ground truth also makes it difficult to assess reconstruction quality. To address these challenges, I first developed a method to generate ground truth super-resolution movies from static SMLM images obtained from long acquisition sequences. I implemented and tested this strategy using both simulated and experimental SMLM images. Second, I extended the ANNA-PALM architecture to 3D data, where the third dimension is time, in order to incorporate temporal information. I used simulations of microtubule dynamics to quantitatively evaluate the reconstruction quality of this approach in comparison with the original 2D ANNA-PALM, and as function of structure velocity and localization rates. [...]
Rizzo, Vittorio. "Ricostruzione di segnali ultrasonici sottocampionati mediante tecniche di compressive sensing e di single-image super-resolution". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.
Ecoto, Dicka Geoffrey. "Modélisation et apprentissage machine learning appliqués à l'estimation des dommages consécutifs à la survenance d'un événement de sécheresse par retrait-gonflement des argiles dans le cadre du régime d'indemnisation des catastrophes naturelles français". Electronic Thesis or Diss., Université Paris Cité, 2023. http://www.theses.fr/2023UNIP7182.
This Ph.D. thesis is dedicated to forecasting the financial impact on insured properties in the event of drought, utilizing methods that merge statistics and machine learning. In this context, "drought" refers to the phenomenon of clay shrinkage and swelling that leads to damage to buildings. The task can be broken down into two sub-problems that we address separately. The first sub-problem focuses on predicting which municipalities will submit a request for the government declaration of natural disaster for a drought event. The second is dedicated to predicting the financial impact of drought events on insured properties located in municipalities that obtained the government declaration of natural disaster for a drought event. For the first sub-problem, we develop, study, and apply an original algorithm to predict requests for the government declaration of natural disaster. The algorithm benefits from two complementary formalizations of the task at hand, approached as both supervised classification and an optimal transport problem. The final predictions are obtained as a geometric mean of these two prediction types. Theoretically, the optimal transport plan can be obtained by applying the iPiano algorithm [Ochs et al., 2015], and we demonstrate that the assumptions underlying its analysis are met. The analysis of the predictions obtained confirms the algorithm's relevance. Regarding the second sub-problem, we develop, investigate, and apply an original aggregation algorithm, inspired by the Super Learner [van der Laan, 2007]. Two challenges must be considered. First, since drought events have only been covered by the French natural disaster compensation scheme since 1989, the number of drought events available for training our algorithm is limited, with each drought event associated with a large dataset. Second, temporal dependence is compounded by spatial dependence, primarily due to geographic and administrative proximity between French municipalities. Based on a dependency modeling using a dependency graph, the theoretical analysis reveals that the brevity of the time series can be compensated if spatial dependence is weak. Once again, the analysis of the predictions obtained underscores the relevance of our algorithm
Reis, Saulo Roberto Sodré dos. "Um método iterativo e escalonável para super-resolução de imagens usando a interpolação DCT e representação esparsa". Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-29122014-113437/.
In a scenario in which the acquisition systems have limited resources or available images do not have good quality, the super-resolution (SR) techniques have become an excellent alternative for improving the image quality. In this thesis, we propose a single-image super-resolution (SR) method that combines the benefits of the DCT interpolation and efficiency of sparse representation method for image reconstruction. Also, the proposed method seeks to take advantage of the improvements already achieved in quality and computational efficiency of the existing SR algorithms. The proposed method implements some improvements in the dictionary training and the reconstruction process. A new dictionary was built by using an unsharp mask technique to characteristics extraction. Simultaneously, this strategy aim to extract more structural information of the low resolution and high resolution patches and reduce the dictionaries size. Another important contribution was the inclusion of an iterative and scalable process by reinserting the HR image obtained of first iteration. This solution aim to improve the quality of the final HR image using a few images in the training set. The results have demonstrated the ability of the proposed method to produce high quality images with reduced computational time.
Ponder, James D. Mattson-Lauters Amy. "Learning from the ads : A triangulated examination of the assault on the last bastion of hegemonic masculinity: the super bowl 2003-2007 /". Diss., A link to full text of this thesis in SOAR, 2007. http://soar.wichita.edu/dspace/handle/10057/1166.
"May 2007." Title from PDF title page (viewed on Dec. 28, 2007). Thesis adviser: Amy Mattson-Lauters. Includes bibliographic references (leaves 111-122).
Zhou, Yi. "Graph-based Mix-out Networks for Video Restoration". Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/21487.
Kuntala, Prashant Kumar. "Optimizing Biomarkers From an Ensemble Learning Pipeline". Ohio University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1503592057943043.
Hatvani, Janka. "Amélioration des images médicales à l'aide de techniques d'apprentissage profond et de factorisation tensorielle". Electronic Thesis or Diss., Toulouse 3, 2021. http://www.theses.fr/2021TOU30304.
The resolution of dental cone beam computed tomography (CBCT) images is imited by detector geometry, sensitivity, patient movement, the reconstruction technique and the need to minimize radiation dose. The corresponding image degradation model assumes that the CBCT image is a blurred (with a point spread function, PSF), downsampled, noisy version of a high resolution image. The quality of the image is crucial for precise diagnosis and treatment planning. The methods proposed in this thesis aim to give a solution for the single image super-resolution (SISR) problem. The algorithms were evaluated on dental CBCT and corresponding highresolution (and high radiation-dose) µCT image pairs of extracted teeth. I have designed a deep learning framework for the SISR problem, applied to CBCT slices. I have tested the U-net and subpixel neural networks, which both improved the PSNR by 21-22 dB, and the Dice coe_cient of the canal segmentation by 1-2.2%, more significantly in the medically critical apical region. I have designed an algorithm for the 3D SISR problem, using the canonical polyadic decomposition of tensors. This implementation conserves the 3D structure of the volume, integrating the factorization-based denoising, deblurring with a known PSF, and upsampling of the image in a lightweight algorithm with a low number of parameters. It outperforms the state-of-the-art 3D reconstruction-based algorithms with two orders of magnitude faster run-time and provides similar PSNR (improvement of 1.2-1.5 dB) and segmentation metrics (Dice coe_cient increased on average to 0.89 and 0.90). Thesis II b: I have implemented a joint alternating recovery of the unknown PSF parameters and of the high-resolution 3D image using CPD-SISR. The algorithm was compared to a state-of-the-art 3D reconstruction-based algorithm, combined with the proposed alternating PSF-optimization. The two algorithms have shown similar improvement in PSNR, but CPD-SISR-blind converged roughly 40 times faster, under 6 minutes both in simulation and on experimental dental computed tomography data. I have proposed a solution for the 3D SISR problem using the Tucker decomposition (TD-SISR). The denoising step is realized _rst by TD in order to mitigate the ill-posedness of the subsequent deconvolution. Compared to CPDSISR the algorithm runs ten times faster. Depending on the amount of noise, higher PSNR (0.3 - 3.5 dB), SSI (0.58 - 2.43%) and segmentation values (Dice coefficient, 2% improvement) were measured. The parameters in TD-SISR are familiar from 2D SVD-based algorithms, so their tuning is easier compared to CPD-SISR
Cho, Myung. "Convex and non-convex optimizations for recovering structured data: algorithms and analysis". Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5922.
Rajnoha, Martin. "Určování podobnosti objektů na základě obrazové informace". Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-437979.
Reza, Katebi. "Nuclear Outbursts in the Centers of Galaxies". Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1573031465540983.
Mohammadiha, Nasser. "Speech Enhancement Using Nonnegative MatrixFactorization and Hidden Markov Models". Doctoral thesis, KTH, Kommunikationsteori, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-124642.
QC 20130916
Holub, Jiří. "Zvýšení kvality fotografie s použitím hlubokých neuronových sítí". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-377334.
McNamara, J. David. "Re-imaging catechesis, an approach to intergenerational cooperative learning in sacrament preparation". Chicago, Ill : McCormick Theological Seminary, 1999. http://www.tren.com.
Withers, Denissia Elizabeth. "Engaging Community Food Systems through Learning Garden Programs: Oregon Food Bank's Seed to Supper Program". PDXScholar, 2012. https://pdxscholar.library.pdx.edu/open_access_etds/609.
Baptiste, Julien. "Problèmes numériques en mathématiques financières et en stratégies de trading". Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLED009.
The aim of this CIFRE thesis is to build a portfolio of intraday algorithmic trading strategies. Instead of considering stock prices as a function of time and a brownian motion, our approach is to identify the main signals affecting market participants when they operate on the market so we can set up a prices model and then build dynamical strategies for portfolio allocation. In a second part, we introduce several works dealing with asian and european option pricing
Defrance, Anne. "Nature du savoir et formulation des définitions dans les cours de mathématiques du secondaire". Doctoral thesis, Universite Libre de Bruxelles, 2010. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210174.
What is the nature of the Mathematics which are taught in secondary education classes (pupils from 12 to 18 years old)? How far does it impair learning mathematics like teachers dream them ?The taught matter shows the features of a text, of a scriptural form showing up differences with the oral form of a society without writing. The formulation of definitions appears to be a powerful tool to perform this analysis. Empirical investigations reveal through four tensions, how hardly the teachers bring their pupils into the learning of a mathematical theory. The analysis of the various ways to validate what they teach leads to show in what serious difficulties is today the teaching of mathematics. A remedy could be the learning of idiomatic competence.
Doctorat en Sciences Psychologiques et de l'éducation
info:eu-repo/semantics/nonPublished
Chiang, Hao-Tien y 姜昊天. "Integrated Learning-Based Super Resolution". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/71289407585998299187.
國立臺灣大學
資訊網路與多媒體研究所
99
Nowadays, the requirement for image resolution increases fiercely. However, the cost of high resolution images obtained from those modern devices is usually expensive, and it is not easy for people to afford. Therefore, the techniques called “super-resolution” enhancing the low resolution image to higher one are quite important. In recent decades, many researches were dedicated in this field and plenty of algorithms were proposed. In this thesis, we present an integrated learning-based super-resolution. Learning-based super-resolution techniques model the co-occurrence patterns between the high and low resolution patches of example images to estimate the missing details for low resolution input. Our system has two parts: training phase and synthesis phase. In the training phase, we construct a database. And in synthesis phase, we retrieve some suitable data and build multi-scale self-similarity model to update the database. We choose corresponding super-resolution algorithms based on different content, and we use back-projection to enforce global reconstruction constraint, and then enhance details of the super-resolved image. Comparing to existing learning-based approaches, our proposed method significantly improves image quality, and the produced super-resolution images have sharp edges and rich details; moreover, the algorithm is very efficient.
Cheng, Yi-Chi y 鄭義錡. "Super Resolution for e-Learning". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/08190088633924527081.
國立臺灣大學
電機工程學研究所
99
Because of the development of image display devices, people require much better quality of images. The high-level image capture devices are still expensive, therefore we usually use software to enhance the quality of images. Super- resolution algorithm is a popular research domain in digital image processing, and the applications are widespread, including the military, surveillance, and so on. Recently, the requirement of on-line digital learning is much higher, but many teachers do not have good image capture devices, they cannot record teaching contents with high quality. In this paper, we propose a method to enhance the normal teaching images, especially for the teaching mode using black board, white board, or projection screen. Though super-resolution algorithm can enhance the image resolution, the large execution time and disregarding the image content are the problems in majority of super-resolution algorithm. In this paper, we use edge detection to estimate the image edge density of small blocks, and use mean shift to implement color segmentation. We can integrate the above information to determine where people pay attention to mostly, where secondly, and where we do not care, and use different complexity algorithms to process them. By the experiment result, we only have to process 20% area of the whole image, and decrease execution time significantly. Simultaneously, we can only get an image output with good quality.
Po-HungKuo y 郭柏宏. "Image Super Resolution Based on Deep Learning". Thesis, 2015. http://ndltd.ncl.edu.tw/handle/35559245822499458738.
國立成功大學
電機工程學系
104
We develop two super resolution methods by different deep learning architecture. The first is the convolutional restricted Boltzmann machine (CRBM), the second is the convolutional neural network (CNN). To accelerate the training procedure, we implement the paralleled training algorithms by a GPU. Our experiments reveals that the super resolution performance of our works is equivalent to that of sparse coding while our processing speed is much faster.
黃士豪. "Local Learning-Based Image Super-Resolution on License Plates". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/92587677163522747428.
國立臺灣師範大學
資訊工程研究所
100
Surveillance camera equipment is mounted, video surveillance systems even more important. Video records are often turned into the police handling assistance resources through license plate recognition. License plate recognition (LPR) usually plays an important role in video surveillance systems. In order to save costs, the resolution of these surveillance cameras is usually not too high, the objective of super- resolution (SR) on license plate images is to enhance the resolution of those images. In this paper, we propose a learning-based SR approach on license plate images. First, several high-resolution (HR) license plate images and the generated corresponding LR ones are first collected as the training images. Next, the clustered HR and LR patch pairs are obtained from the training images. Then, license plates are extracted from a LR traffic surveillance image and cut into overlapped patches, and the clustered HR and LR patch pairs are used to generate the HR patch for each cut LR patch by using locally linear embedding (LLE) algorithm. Finally, the HR license plate images can be reconstruction. Preliminary experiments on realistic image data demonstrate the applicability of the proposed approach.