1 resultado para Representation and information retrieval technologies
em Universidade Federal de Uberlândia
Filtro por publicador
- JISC Information Environment Repository (2)
- Repository Napier (3)
- Aberystwyth University Repository - Reino Unido (8)
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (3)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (3)
- Applied Math and Science Education Repository - Washington - USA (1)
- Aquatic Commons (4)
- Archive of European Integration (47)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (2)
- Aston University Research Archive (32)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (5)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (2)
- Biblioteca Digital de Teses e Dissertações Eletrônicas da UERJ (2)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (37)
- Boston University Digital Common (3)
- Brock University, Canada (3)
- Bucknell University Digital Commons - Pensilvania - USA (1)
- Bulgarian Digital Mathematics Library at IMI-BAS (8)
- CaltechTHESIS (1)
- Cambridge University Engineering Department Publications Database (25)
- CentAUR: Central Archive University of Reading - UK (40)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (8)
- Cochin University of Science & Technology (CUSAT), India (5)
- Comissão Econômica para a América Latina e o Caribe (CEPAL) (23)
- CORA - Cork Open Research Archive - University College Cork - Ireland (2)
- Corvinus Research Archive - The institutional repository for the Corvinus University of Budapest (2)
- CUNY Academic Works (2)
- Dalarna University College Electronic Archive (1)
- Deakin Research Online - Australia (87)
- Department of Computer Science E-Repository - King's College London, Strand, London (14)
- DI-fusion - The institutional repository of Université Libre de Bruxelles (1)
- Digital Commons - Michigan Tech (3)
- Digital Commons - Montana Tech (1)
- Digital Commons @ DU | University of Denver Research (3)
- Digital Commons at Florida International University (8)
- Digital Peer Publishing (1)
- DigitalCommons@The Texas Medical Center (2)
- DigitalCommons@University of Nebraska - Lincoln (3)
- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (1)
- DRUM (Digital Repository at the University of Maryland) (4)
- Duke University (2)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (5)
- FUNDAJ - Fundação Joaquim Nabuco (1)
- Funes: Repositorio digital de documentos en Educación Matemática - Colombia (1)
- Glasgow Theses Service (1)
- Greenwich Academic Literature Archive - UK (4)
- Helda - Digital Repository of University of Helsinki (6)
- Indian Institute of Science - Bangalore - Índia (8)
- Instituto Politécnico do Porto, Portugal (4)
- Martin Luther Universitat Halle Wittenberg, Germany (1)
- Massachusetts Institute of Technology (5)
- Memoria Académica - FaHCE, UNLP - Argentina (3)
- National Center for Biotechnology Information - NCBI (7)
- Portal de Revistas Científicas Complutenses - Espanha (1)
- QSpace: Queen's University - Canada (2)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (55)
- Queensland University of Technology - ePrints Archive (179)
- ReCiL - Repositório Científico Lusófona - Grupo Lusófona, Portugal (2)
- Repositório Aberto da Universidade Aberta de Portugal (1)
- Repositorio Académico de la Universidad Nacional de Costa Rica (9)
- Repositorio de la Universidad de Cuenca (1)
- Repositório digital da Fundação Getúlio Vargas - FGV (4)
- Repositório Digital da UNIVERSIDADE DA MADEIRA - Portugal (1)
- Repositório Institucional da Universidade Estadual de São Paulo - UNESP (1)
- Repositório Institucional da Universidade Federal do Rio Grande do Norte (1)
- Repositorio Institucional de la Universidad de Málaga (2)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (33)
- Repositorio Institucional Universidad de Medellín (2)
- Repositorio Institucional Universidad EAFIT - Medelin - Colombia (1)
- Royal College of Art Research Repository - Uninet Kingdom (1)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (4)
- School of Medicine, Washington University, United States (1)
- Universidad de Alicante (9)
- Universidad del Rosario, Colombia (1)
- Universidad Politécnica de Madrid (16)
- Universidade de Lisboa - Repositório Aberto (1)
- Universidade Federal de Uberlândia (1)
- Universidade Federal do Pará (1)
- Universidade Federal do Rio Grande do Norte (UFRN) (2)
- Universitat de Girona, Spain (1)
- Université de Montréal (2)
- Université de Montréal, Canada (6)
- University of Canberra Research Repository - Australia (2)
- University of Michigan (90)
- University of Queensland eSpace - Australia (30)
- University of Southampton, United Kingdom (8)
- University of Washington (2)
- WestminsterResearch - UK (4)
Resumo:
lmage super-resolution is defined as a class of techniques that enhance the spatial resolution of images. Super-resolution methods can be subdivided in single and multi image methods. This thesis focuses on developing algorithms based on mathematical theories for single image super resolution problems. lndeed, in arder to estimate an output image, we adopta mixed approach: i.e., we use both a dictionary of patches with sparsity constraints (typical of learning-based methods) and regularization terms (typical of reconstruction-based methods). Although the existing methods already per- form well, they do not take into account the geometry of the data to: regularize the solution, cluster data samples (samples are often clustered using algorithms with the Euclidean distance as a dissimilarity metric), learn dictionaries (they are often learned using PCA or K-SVD). Thus, state-of-the-art methods still suffer from shortcomings. In this work, we proposed three new methods to overcome these deficiencies. First, we developed SE-ASDS (a structure tensor based regularization term) in arder to improve the sharpness of edges. SE-ASDS achieves much better results than many state-of-the- art algorithms. Then, we proposed AGNN and GOC algorithms for determining a local subset of training samples from which a good local model can be computed for recon- structing a given input test sample, where we take into account the underlying geometry of the data. AGNN and GOC methods outperform spectral clustering, soft clustering, and geodesic distance based subset selection in most settings. Next, we proposed aSOB strategy which takes into account the geometry of the data and the dictionary size. The aSOB strategy outperforms both PCA and PGA methods. Finally, we combine all our methods in a unique algorithm, named G2SR. Our proposed G2SR algorithm shows better visual and quantitative results when compared to the results of state-of-the-art methods.