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Dissertations / Theses on the topic 'Super learning'

Author: Grafiati

Published: 6 July 2024

Last updated: 31 July 2025

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1

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.

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Context. Developing an Artificial Intelligence (AI) agent that canpredict and act in all possible situations in the dynamic environmentsthat modern video games often consists of is on beforehand nearly im-possible and would cost a lot of money and time to create by hand. Bycreating a learning AI agent that could learn by itself by studying itsenvironment with the help of Reinforcement Learning (RL) it wouldsimplify this task. Another wanted feature that often is required is AIagents with a natural acting behavior and a try to solve that problemcould be to imitating a human by using Imitation L

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2

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.

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Thesis (Ph. D.)--University of California, San Diego, 2010.<br>Title from first page of PDF file (viewed April 14, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 96-102).

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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.

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Multi-frame image super-resolution is a procedure which takes several noisy low-resolution images of the same scene, acquired under different conditions, and processes them together to synthesize one or more high-quality super-resolution images, with higher spatial frequency, and less noise and image blur than any of the original images. The inputs can take the form of medical images, surveillance footage, digital video, satellite terrain imagery, or images from many other sources. This thesis focuses on Bayesian methods for multi-frame super-resolution, which use a prior distribution over the

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Ouyang, Wei. "Deep Learning for Advanced Microscopy." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCC174/document.

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Contexte: La microscopie joue un rôle important en biologie depuis plusieurs siècles, mais sa résolution a longtemps été limitée à environ 250 nm, de sorte que nombre de structures biologiques (virus, vésicules, pores nucléaires, synapses) ne pouvaient être résolues. Au cours de la dernière décennie, plusieurs méthodes de super-résolution ont été développées pour dépasser cette limite. Parmi ces techniques, les plus puissantes et les plus utilisées reposent sur la localisation de molécules uniques (microscopie à localisation de molécule unique, ou SMLM), comme PALM et STORM. En localisant préc

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Yelibi, Lionel. "Introduction to fast Super-Paramagnetic Clustering." Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31332.

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We map stock market interactions to spin models to recover their hierarchical structure using a simulated annealing based Super-Paramagnetic Clustering (SPC) algorithm. This is directly compared to a modified implementation of a maximum likelihood approach to fast-Super-Paramagnetic Clustering (f-SPC). The methods are first applied standard toy test-case problems, and then to a dataset of 447 stocks traded on the New York Stock Exchange (NYSE) over 1249 days. The signal to noise ratio of stock market correlation matrices is briefly considered. Our result recover approximately clusters represen

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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.

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An increasing number of applications require high-resolution images in situations where the access to the sensor and the knowledge of its specifications are limited. In this thesis, the problem of blind super-resolution is addressed, here defined as the estimation of a high-resolution image from one or more low-resolution inputs, under the condition that the degradation model parameters are unknown. The assessment of super-resolved results, using objective measures of image quality, is also addressed.<br>Learning-based methods have been successfully applied to the single frame super-resolution

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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.

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Super-Resolution is a widely studied problem in the field of computer vision, where the purpose is to increase the resolution of, or super-resolve, image data. In Video Super-Resolution, maintaining temporal coherence for consecutive video frames requires fusing information from multiple frames to super-resolve one frame. Current deep learning methods perform video super-resolution, yet most of them focus on working with natural datasets. In this thesis, we use a recurrent back-projection network for working with a dataset of computer-generated graphics, with example applications including ups

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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.

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Cette thèse porte sur les problèmes de sécurité sur le réseau électrique français exploité par RTE, le Gestionnaire de Réseau de Transport (GRT). Les progrès en matière d'énergie durable, d'efficacité du marché de l'électricité ou de nouveaux modes de consommation poussent les GRT à exploiter le réseau plus près de ses limites de sécurité. Pour ce faire, il est essentiel de rendre le réseau plus "intelligent". Pour s'attaquer à ce problème, ce travail explore les avantages des réseaux neuronaux artificiels. Nous proposons de nouveaux algorithmes et architectures d'apprentissage profond pour ai

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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.

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Segmentation of the knee cartilage is an important step for surgery planning and manufacturing patient-specific prostheses. What has been a promising technology in recent years is deep learning-based super-resolution methods that are composed of feed-forward models which have been successfully applied on natural and medical images. This thesis aims to test the feasibility to super-resolve thick slice 2D sequence acquisitions and acquire sufficient segmentation accuracy of the articular cartilage in the knee. The investigated approaches are single- and multi-contrast super-resolution, where the

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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/.

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The increasing amount of data produced by modern infrastructures requires instruments of analysis more and more precise, quick, and efficient. For these reasons in the last decades, Machine Learning (ML) and Deep Learning (DL) techniques saw exponential growth in publications and research from the scientific community. In this work are proposed two new frameworks for Deep Learning: Byron written in C++, for fast analysis in a parallelized CPU environment, and NumPyNet written in Python, which provides a clear and understandable interface on deep learning tailored around readability. Byron wil

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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.

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With super-resolution (SR) we refer to a class of techniques that enhance the spatial resolution of images and videos. SR algorithms can be of two kinds: multi-frame methods, where multiple low-resolution images are aggregated to form a unique high-resolution image, and single-image methods, that aim at upscaling a single image. This thesis focuses on developing theory and algorithms for the single-image SR problem. In particular, we adopt the so called example-based approach, where the output image is estimated with machine learning techniques, by using the information contained in a dictiona

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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.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (page 29).<br>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 performa

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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.

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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.

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L’objectif de cette thèse est d’élaborer des méthodes efficaces pour le débruitage et la super-résolution afin d’améliorer la qualité et la résolution spatiale des images médicales. En particulier, nous sommes motivés par le challenge d’intégrer le problème de débruitage et de super-résolution dans la même formulation. Nos méthodes utilisent des images standards ou d’exemples localisées à proximité de l’image considérée pour le débruitage et/ou pour la super-résolution. Pour le problème de débruitage, nous introduisons trois nouvelles méthodes qui permettent de réduire certains bruits couramme

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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.

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This work presents my research about the use of Convolutional Neural Network (CNNs) for transfer learning through its application for colonic polyp classification and iris super-resolution. Traditionally, machine learning methods use the same feature space and the same distribution for training and testing the tools. Several problems in this approach can emerge as, for example, when the number of samples for training (especially in a supervised training) is limited. In the medical field, this problem is recurrent mainly because obtaining a database large enough with appropriate annotatio

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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.

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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.

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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/.

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L'argomento principale di questa tesi è il problema di super risoluzione applicato all'ambito dell'imaging. Lo scopo è quello di fornire una panoramica generale sul problema e sugli approcci possibili, e di presentare dei modelli di soluzione basati sull'uso di reti neurali arti�ficiali.

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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.

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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.

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Over the past few decades, new commercial markets and user requirements of video and image processing have increased dramatically. Target detection with accurate trajectory in real-time settings and high-resolution image performance are now essential in the computer vision domain. Meanwhile, evolutionary algorithms also play an important role in the optimisation and estimation of video target tracking and image processing. Some effects and challenges need to be addressed within videos and images to provide precise results in complex and changeable conditions. The aim of this research is to dev

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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.

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Orientador: Hélio Pedrini<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação<br>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<br>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 d

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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.

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Image Super resolution is a widely-studied problem in computer vision, where the objective is to convert a lowresolution image to a high resolution image. Conventional methods for achieving super-resolution such as image priors, interpolation, sparse coding require a lot of pre/post processing and optimization. Recently, deep learning methods such as convolutional neural networks and generative adversarial networks are being used to perform super-resolution with results competitive to the state of the art but none of them have been used on microscopy images. In this thesis, a generative advers

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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/.

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In diversi settori sono necessarie immagini ad alta risoluzione. Per risoluzione non intendiamo solamente le dimensioni spaziali in pixel dell'immagine ma anche la qualità dell'immagine stessa, priva di distorsioni e/o rumore. Solitamente immagini di questo tipo si possono ottenere usando apparecchiature di acquisizione munite di sensori ad alta precisione, in grado di convertire in digitale i dati analogici. Spesso, per limitazioni fisiche ed economiche, la qualità raggiunta dagli strumenti è ben lontana da quella richiesta per le varie applicazioni. Per risolvere questo problema sono state s

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Zandavi, Seid Miad. "Indoor Autonomous Flight Using Deep Learning-Based Image Understanding Techniques." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/22893.

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Indoor autonomous flight using artificial intelligence (AI) and machine learning techniques is presented. Flying inside a building without a positioning system requires a particular framework to connect computer vision, machine learning, control theory, and AI. The framework consists of six modules/disciplines presented to support indoor autonomous flight: optimization, state estimation, control, object detection, deep learning, and guidance. In this regard, the mathematical model of a quadcopter/drone is derived from an accurate model with a high level of fidelity by considering the non-linea

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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.

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There are many clinical situations where magnetic resonance imaging (MRI) is preferable over other imaging modalities, while the major disadvantage is the relatively long scan time. Due to limited resources, this means that not all patients can be offered an MRI scan, even though it could provide crucial information. It can even be deemed unsafe for a critically ill patient to undergo the examination. In MRI, there is a trade-off between resolution, signal-to-noise ratio (SNR) and the time spent gathering data. When time is of utmost importance, we seek other methods to increase the resolution

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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.

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Generative Adversarial Networks have been used to obtain state-of-the-art results for low-level computer vision tasks like single image super-resolution, however, they are notoriously difficult to train due to the instability related to the competing minimax framework. Additionally, traditional ensembling mechanisms cannot be effectively applied with these types of networks due to the resources they require at inference time and the complexity of their architectures. In this thesis an alternative method to create ensembles of individual, more stable and easier to train, models by using interpo

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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.

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L'objectif de cette thèse est d'étudier le comportement de différentes représentations d'images, notamment apprentissage profond, dans le contexte d'application en imagerie médicale. Le but est de développer une méthode unifiée efficace pour les applications visées que sont la super résolution, la segmentation et la synthèse. La super-résolution est un procès d'estimation d'une image haute-résolution à partir d'une ou plusieurs images basses résolutions. Dans cette thèse, nous nous concentrons sur la super résolutionunique, c'est-à-dire que l'image haute résolution (HR) est estimée par une ima

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Peyrard, Clément. "Single image super-resolution based on neural networks for text and face recognition." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEI083/document.

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Cette thèse porte sur les méthodes de super-résolution (SR) pour l’amélioration des performances des systèmes de reconnaissance automatique (OCR, reconnaissance faciale). Les méthodes de Super-Résolution (SR) permettent de générer des images haute résolution (HR) à partir d’images basse résolution (BR). Contrairement à un rééchantillonage par interpolation, elles restituent les hautes fréquences spatiales et compensent les artéfacts (flou, crénelures). Parmi elles, les méthodes d’apprentissage automatique telles que les réseaux de neurones artificiels permettent d’apprendre et de modéliser la

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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.

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In healthcare, a large number of medical data has emerged. To effectively use these data to improve healthcare outcomes, clinicians need to identify the relevant measures and apply the correct analysis methods for the type of data at hand. In this dissertation, we present various machine learning (ML) and data mining (DM) methods that could be applied to the type of data sets that are available in the healthcare area. The first part of the dissertation investigates DM methods on healthcare or medical data to find significant information in the form of rules. Class association rule mining, a v

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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.

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La microscopie optique est une technique centrale en biologie cellulaire, mais la diffraction limite sa résolution à environ 200-300 nm. Par conséquent, de nombreuses structures moléculaires telles que les virus, les pores nucléaires ou les microtubules, ne peuvent pas être résolues. La microscopie de localisation à molécule unique (SMLM) offre une résolution spatiale élevée (20 nm ou mieux), permettant de résoudre les structures biologiques à l'échelle moléculaire. Cependant, la SMLM nécessite l'acquisition de plusieurs milliers d’images à base résolution, ce qui limite ses applications aux c

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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.

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La scansione laser ad ultrasuoni è un metodo di monitoraggio strutturale molto efficace nell'ambito del rilevamento dei danni, grazie alla sua natura non distruttiva, alla sensibilità al danno locale e all'elevata risoluzione spaziale. Le immagini ricostruite a seguito della propagazione di onde ultrasoniche nella struttura di interesse consentono di riscontrare la presenza di eventuali danni. Tuttavia, la scansione laser ad ultrasuoni richiede tempi di scansione molto lunghi per ottenere un elevata risoluzione spaziale e un adeguato rapporto segnale rumore, necessari al rilevamento dei danni.

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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.

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Cette thèse est consacrée à l'anticipation de l'impact financier sur les biens assurés de la survenance d'un événement de sécheresse grâce au recours à des méthodes au croisement de la statistique et du machine learning. Le terme sécheresse désigne ici le phénomène de retrait-gonflement des argiles provoquant des dommages aux bâtiments. L'exercice peut être décomposé en deux sous-problèmes que nous abordons tour à tour. Le premier sous-problème considère plus spécifiquement la tâche consistant à prédire quelles communes formuleront une demande de reconnaissance de l'état de catastrophe naturel

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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/.

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Num cenário em que dispositivos de aquisição de imagens e vídeo possuem recursos limitados ou as imagens disponíveis não possuem boa qualidade, as técnicas de super-resolução (SR) apresentam uma excelente alternativa para melhorar a qualidade das imagens. Nesta tese é apresentada uma proposta para super-resolução de imagem única que combina os benefícios da interpolação no domínio da transformada DCT e a eficiência dos métodos de reconstrução baseados no conceito de representação esparsa de sinais. A proposta busca aproveitar as melhorias já alcançadas na qualidade e eficiência computacional d

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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.

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Thesis (M.A.)--Wichita State University, College of Liberal Arts and Sciences., Elliott School of Communication.<br>"May 2007." Title from PDF title page (viewed on Dec. 28, 2007). Thesis adviser: Amy Mattson-Lauters. Includes bibliographic references (leaves 111-122).

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Zhou, Yi. "Graph-based Mix-out Networks for Video Restoration." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/21487.

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The video restoration task aims to generate clear and high-quality videos from those noisy or blurry low-quality videos. Different from image restoration tasks, the temporal information is an additional data dimension in videos and it plays a key role in video restoration task. Therefore, how to effectively take the full advantage of the inter-frame temporal information is a challenging problem for recovering video quality. Current approaches attempt to either warp the features based on the estimated motion modality before post-processing or fuse the features (e.g. concatenation) only once at

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Kuntala, Prashant Kumar. "Optimizing Biomarkers From an Ensemble Learning Pipeline." Ohio University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1503592057943043.

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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.

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La résolution spatiale des images acquises par tomographie volumique à faisceau conique (CBCT) est limitée par la géométrie des capteurs, leur sensibilité, les mouvements du patient, les techniques de reconstruction d'images et la limitation de la dose de rayonnement. Le modèle de dégradation d'image considéré dans cette thèse consiste en un opérateur de ou avec la fonction d'étalement du système d'imagerie (PSF), un opérateur de décimation, et du bruit, qui relient les volumes CBCT à une image 3D super-résolue à estimer. Les méthodes proposées dans cette thèse (SISR - single image super-résol

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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.

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Optimization theories and algorithms are used to efficiently find optimal solutions under constraints. In the era of “Big Data”, the amount of data is skyrocketing,and this overwhelms conventional techniques used to solve large scale and distributed optimization problems. By taking advantage of structural information in data representations, this thesis offers convex and non-convex optimization solutions to various large scale optimization problems such as super-resolution, sparse signal processing,hypothesis testing, machine learning, and treatment planning for brachytherapy. Super-resolution

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Archambault, Théo. "Deep learning for sea surface height reconstruction from multi-variate satellite observations." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS253.

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Cette thèse de doctorat porte sur la reconstruction d'images satellites de la surface de l'océan à partir de mesures éparses et bruitées. Son objectif est l'estimation de la hauteur de la mer (SSH), une variable importante pour approximer les courants de surface. Elle est actuellement mesurée par des altimètres pointant au nadir, laissant de nombreuses zones non observées. Les cartes complètes de SSH sont produites en utilisant des interpolations optimales linéaires présentant une faible résolution effective.D'autre part, la température de surface de la mer (SST) est observée sur des zones plu

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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.

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Monitoring of public areas and their automatic real-time processing became increasingly significant due to the changing security situation in the world. However, the problem is an analysis of low-quality records, where even the state-of-the-art methods fail in some cases. This work investigates an important area of image similarity – biometric identification based on face image. The work deals primarily with the face super-resolution from a sequence of low-resolution images and it compares this approach to the single-frame methods, that are still considered as the most accurate. A new dataset

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Reza, Katebi. "Nuclear Outbursts in the Centers of Galaxies." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1573031465540983.

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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.

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Reducing interference noise in a noisy speech recording has been a challenging task for many years yet has a variety of applications, for example, in handsfree mobile communications, in speech recognition, and in hearing aids. Traditional single-channel noise reduction schemes, such as Wiener filtering, do not work satisfactorily in the presence of non-stationary background noise. Alternatively, supervised approaches, where the noise type is known in advance, lead to higher-quality enhanced speech signals. This dissertation proposes supervised and unsupervised single-channel noise reduction al

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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.

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This diploma thesis deals with image super-resolution with conservation of good quality. Firstly, there are described state of the art methods dealing with this problem, as well as principles of neural networks with focus on convolutional ones. Finally, there is described a few models of convolutional neural network for image super-resolution to double size, which have been trained, tested and compared on newly created database with pictures of people.

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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.

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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.

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The purpose of this study was to discover whether learning garden programs increase access to locally grown foods and successfully empower and include food insecure populations. This study examined the Oregon Food Bank's Seed to Supper program which situates garden-based learning in food insecure communities. Through a mixed-methods community-based research process, this study found that community building, learner empowerment and sustainability leadership in place-based learning garden programs increased access to locally grown foods for food insecure populations. When food insecure populatio

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Baptiste, Julien. "Problèmes numériques en mathématiques financières et en stratégies de trading." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLED009.

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Le but de cette thèse CIFRE est de construire un portefeuille de stratégies de trading algorithmique intraday. Au lieu de considérer les prix comme une fonction du temps et d'un aléa généralement modélisé par un mouvement brownien, notre approche consiste à identifier les principaux signaux auxquels sont sensibles les donneurs d'ordres dans leurs prises de décision puis alors de proposer un modèle de prix afin de construire des stratégies dynamiques d'allocation de portefeuille. Dans une seconde partie plus académique, nous présentons des travaux de pricing d'options européennes et asiatiques<

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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.

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Quelle est la nature des mathématiques enseignées dans les classes de l’enseignement secondaire? Dans quelle mesure l’enseignement des compétences n’handicape-t-il pas l’accès à des mathématiques telles qu’en rêvent les enseignants ?Ce qu’on enseigne a les caractéristiques d’un texte, d’une forme scripturale qui présente des différences avec la forme orale d’une société qui ne connaît pas l’écriture. Il apparaît que la formulation des définitions présente un outil performant pour cette analyse. Les investigations empiriques dévoilent, à travers quatre tensions, les difficultés qu’ont les ensei

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Chiang, Hao-Tien, and 姜昊天. "Integrated Learning-Based Super Resolution." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/71289407585998299187.

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碩士<br>國立臺灣大學<br>資訊網路與多媒體研究所<br>99<br>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 t

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Cheng, Yi-Chi, and 鄭義錡. "Super Resolution for e-Learning." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/08190088633924527081.

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碩士<br>國立臺灣大學<br>電機工程學研究所<br>99<br>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

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Po-HungKuo and 郭柏宏. "Image Super Resolution Based on Deep Learning." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/35559245822499458738.

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碩士<br>國立成功大學<br>電機工程學系<br>104<br>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.

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