1 resultado para Non-Local Model
em Universidade Federal de Uberlândia
Filtro por publicador
- Repository Napier (1)
- Aberdeen University (2)
- Abertay Research Collections - Abertay University’s repository (1)
- Academic Archive On-line (Stockholm University; Sweden) (3)
- Academic Research Repository at Institute of Developing Economies (3)
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (5)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (23)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (9)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (9)
- Archimer: Archive de l'Institut francais de recherche pour l'exploitation de la mer (1)
- Archive of European Integration (5)
- Aston University Research Archive (25)
- Australian Council for Educational Research (1)
- Biblioteca de Teses e Dissertações da USP (1)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (13)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (111)
- Biblioteca Virtual del Sistema Sanitario Público de Andalucía (BV-SSPA), Junta de Andalucía. Consejería de Salud y Bienestar Social, Spain (1)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (43)
- Bucknell University Digital Commons - Pensilvania - USA (2)
- Bulgarian Digital Mathematics Library at IMI-BAS (5)
- CaltechTHESIS (2)
- CentAUR: Central Archive University of Reading - UK (56)
- Central European University - Research Support Scheme (1)
- CiencIPCA - Instituto Politécnico do Cávado e do Ave, Portugal (4)
- Cochin University of Science & Technology (CUSAT), India (4)
- Coffee Science - Universidade Federal de Lavras (2)
- Comissão Econômica para a América Latina e o Caribe (CEPAL) (1)
- Consorci de Serveis Universitaris de Catalunya (CSUC), Spain (41)
- Cor-Ciencia - Acuerdo de Bibliotecas Universitarias de Córdoba (ABUC), Argentina (1)
- CORA - Cork Open Research Archive - University College Cork - Ireland (1)
- Corvinus Research Archive - The institutional repository for the Corvinus University of Budapest (3)
- CUNY Academic Works (1)
- Dalarna University College Electronic Archive (2)
- Department of Computer Science E-Repository - King's College London, Strand, London (1)
- Digital Commons - Michigan Tech (3)
- Digital Commons at Florida International University (6)
- DigitalCommons - The University of Maine Research (1)
- DigitalCommons@The Texas Medical Center (7)
- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (13)
- Duke University (2)
- Earth Simulator Research Results Repository (1)
- FUNDAJ - Fundação Joaquim Nabuco (1)
- Illinois Digital Environment for Access to Learning and Scholarship Repository (2)
- Institutional Repository of Leibniz University Hannover (1)
- Instituto Politécnico de Bragança (1)
- Instituto Politécnico do Porto, Portugal (17)
- Iowa Publications Online (IPO) - State Library, State of Iowa (Iowa), United States (1)
- Lume - Repositório Digital da Universidade Federal do Rio Grande do Sul (1)
- Martin Luther Universitat Halle Wittenberg, Germany (1)
- Massachusetts Institute of Technology (6)
- Memorial University Research Repository (1)
- National Center for Biotechnology Information - NCBI (1)
- Nottingham eTheses (6)
- Publishing Network for Geoscientific & Environmental Data (9)
- QSpace: Queen's University - Canada (2)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (6)
- ReCiL - Repositório Científico Lusófona - Grupo Lusófona, Portugal (1)
- Repositório Aberto da Universidade Aberta de Portugal (1)
- Repositorio Académico de la Universidad Nacional de Costa Rica (1)
- Repositório Científico da Universidade de Évora - Portugal (1)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (22)
- Repositório da Produção Científica e Intelectual da Unicamp (5)
- Repositório da Universidade Federal do Espírito Santo (UFES), Brazil (2)
- Repositório de Administração Pública (REPAP) - Direção-Geral da Qualificação dos Trabalhadores em Funções Públicas (INA), Portugal (1)
- Repositório digital da Fundação Getúlio Vargas - FGV (7)
- Repositório do Centro Hospitalar de Lisboa Central, EPE - Centro Hospitalar de Lisboa Central, EPE, Portugal (3)
- Repositório Institucional da Universidade de Aveiro - Portugal (1)
- Repositório Institucional da Universidade Estadual de São Paulo - UNESP (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (102)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (31)
- SAPIENTIA - Universidade do Algarve - Portugal (1)
- Scielo Saúde Pública - SP (35)
- Scottish Institute for Research in Economics (SIRE) (SIRE), United Kingdom (13)
- Universidad de Alicante (9)
- Universidad del Rosario, Colombia (1)
- Universidad Politécnica de Madrid (24)
- Universidade Complutense de Madrid (4)
- Universidade do Minho (18)
- Universidade dos Açores - Portugal (6)
- Universidade Federal de Uberlândia (1)
- Universidade Federal do Pará (2)
- Universidade Federal do Rio Grande do Norte (UFRN) (7)
- Universidade Metodista de São Paulo (1)
- Universidade Técnica de Lisboa (1)
- Universitat de Girona, Spain (5)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (4)
- Université de Lausanne, Switzerland (53)
- Université de Montréal (1)
- Université de Montréal, Canada (12)
- University of Canberra Research Repository - Australia (1)
- University of Connecticut - USA (2)
- University of Michigan (26)
- University of Queensland eSpace - Australia (78)
- University of Washington (1)
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.