Quantum computing application in super-resolution

Detalhes bibliográficos
Autor(a) principal: Alves, Ystallonne Carlos da Silva
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: por
Título da fonte: Repositório Institucional da UFRN
Texto Completo: https://repositorio.ufrn.br/jspui/handle/123456789/28123
Resumo: Super-Resolution (SR) is a technique that has been exhaustively exploited and incorporates strategic aspects to image processing. As quantum computers gradually evolve and provide unconditional proof of a computational advantage at solving intractable problems over their classical counterparts, quantum computing emerges with the compelling argument of offering exponential speed-up to process computationally expensive operations. Envisioning the design of parallel, quantum-ready algorithms for near-term noisy devices and igniting Rapid and Accurate Image Super Resolution (RAISR), an implementation applying variational quantum computation is demonstrated for enhancing degraded imagery. This study proposes an approach that combines the benefits of RAISR, a non hallucinating and computationally efficient method, and Variational Quantum Eigensolver (VQE), a hybrid classical-quantum algorithm, to conduct SR with the support of a quantum computer, while preserving quantitative performance in terms of Image Quality Assessment (IQA). It covers the generation of additional hash-based filters learned with the classical implementation of the SR technique, in order to further explore performance improvements, produce images that are significantly sharper, and induce the learning of more powerful upscaling filters with integrated enhancement effects. As a result, it extends the potential of applying RAISR to improve low quality assets generated by low cost cameras, as well as fosters the eventual implementation of robust image enhancement methods powered by the use of quantum computation.
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spelling Alves, Ystallonne Carlos da SilvaSantos, Araken de MedeirosAbreu, Marjory Cristiany da CostaCarvalho, Bruno Motta de2019-12-04T22:14:36Z2019-12-04T22:14:36Z2019-07-31ALVES, Ystallonne Carlos da Silva. Quantum computing application in super-resolution. 2019. 106f. Dissertação (Mestrado em Sistemas e Computação) - Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2019.https://repositorio.ufrn.br/jspui/handle/123456789/28123Super-Resolution (SR) is a technique that has been exhaustively exploited and incorporates strategic aspects to image processing. As quantum computers gradually evolve and provide unconditional proof of a computational advantage at solving intractable problems over their classical counterparts, quantum computing emerges with the compelling argument of offering exponential speed-up to process computationally expensive operations. Envisioning the design of parallel, quantum-ready algorithms for near-term noisy devices and igniting Rapid and Accurate Image Super Resolution (RAISR), an implementation applying variational quantum computation is demonstrated for enhancing degraded imagery. This study proposes an approach that combines the benefits of RAISR, a non hallucinating and computationally efficient method, and Variational Quantum Eigensolver (VQE), a hybrid classical-quantum algorithm, to conduct SR with the support of a quantum computer, while preserving quantitative performance in terms of Image Quality Assessment (IQA). It covers the generation of additional hash-based filters learned with the classical implementation of the SR technique, in order to further explore performance improvements, produce images that are significantly sharper, and induce the learning of more powerful upscaling filters with integrated enhancement effects. As a result, it extends the potential of applying RAISR to improve low quality assets generated by low cost cameras, as well as fosters the eventual implementation of robust image enhancement methods powered by the use of quantum computation.Super-Resolução (SR) é uma técnica exaustivamente explorada e incorpora aspectos estratégicos ao processamento de imagens. À medida que os computadores quânticos gradualmente evoluem e fornecem provas incondicionais de uma vantagem computacional na solução de problemas intratáveis com relação aos homólogos clássicos, a computação quântica emerge com o argumento convincente de oferecer aceleração exponencial para processar operações computacionalmente dispendiosas. Vislumbrando o design de algoritmos paralelos, quantum-ready, para dispositivos ruidosos de curto prazo e iniciando com a Super-Resolução Rápida e Acurada de Imagem (Rapid and Accurate Image Super Resolution – RAISR), uma implementação aplicando computação quântica variacional é demonstrada para aprimorar imagens degradadas. Este estudo propõe uma abordagem que combina os benefícios de RAISR, um método não alucinante e computacionalmente eficiente, e o Eigensolver Variacional Quântico (Variational Quantum Eigensolver – VQE), um algoritmo híbrido clássico-quântico, para conduzir SR com o suporte de um computador quântico, preservando o desempenho quantitativo em termos de Avaliação da Qualidade de Imagem (Image Quality Assessment – IQA). Ele abrange a geração de filtros adicionais baseados em hash, aprendidos com a implementação clássica da técnica de SR, para explorar mais melhorias de desempenho, produzir imagens que são significativamente mais nítidas e induzir o aprendizado de filtros de ampliação mais poderosos com efeitos de aprimoramento integrados. Como resultado, amplia o potencial de aplicação de RAISR para melhorar os ativos de baixa qualidade gerados por câmeras de baixo custo, bem como promove a eventual implementação de métodos robustos de aprimoramento de imagens impulsionados através do uso de computação quântica.CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAOQuantum computingSuper-resolutionImagery enhancementQuantum computing application in super-resolutionAplicação de computação quântica em super-resoluçãoinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisPROGRAMA DE PÓS-GRADUAÇÃO EM SISTEMAS E COMPUTAÇÃOUFRNBrasilinfo:eu-repo/semantics/openAccessporreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNTEXTQuantumcomputingapplication_Alves_2019.pdf.txtQuantumcomputingapplication_Alves_2019.pdf.txtExtracted texttext/plain175031https://repositorio.ufrn.br/bitstream/123456789/28123/2/Quantumcomputingapplication_Alves_2019.pdf.txt499ec69198ebdc21b1bcc35a2f4fae38MD52THUMBNAILQuantumcomputingapplication_Alves_2019.pdf.jpgQuantumcomputingapplication_Alves_2019.pdf.jpgGenerated Thumbnailimage/jpeg1182https://repositorio.ufrn.br/bitstream/123456789/28123/3/Quantumcomputingapplication_Alves_2019.pdf.jpg633d7cc7cf886b5ea11cde35aa4cea21MD53ORIGINALQuantumcomputingapplication_Alves_2019.pdfapplication/pdf56815536https://repositorio.ufrn.br/bitstream/123456789/28123/1/Quantumcomputingapplication_Alves_2019.pdf99564ae25e45e974d9bccbfad977036dMD51123456789/281232019-12-08 02:26:33.134oai:https://repositorio.ufrn.br:123456789/28123Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2019-12-08T05:26:33Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false
dc.title.pt_BR.fl_str_mv Quantum computing application in super-resolution
dc.title.alternative.pt_BR.fl_str_mv Aplicação de computação quântica em super-resolução
title Quantum computing application in super-resolution
spellingShingle Quantum computing application in super-resolution
Alves, Ystallonne Carlos da Silva
CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
Quantum computing
Super-resolution
Imagery enhancement
title_short Quantum computing application in super-resolution
title_full Quantum computing application in super-resolution
title_fullStr Quantum computing application in super-resolution
title_full_unstemmed Quantum computing application in super-resolution
title_sort Quantum computing application in super-resolution
author Alves, Ystallonne Carlos da Silva
author_facet Alves, Ystallonne Carlos da Silva
author_role author
dc.contributor.authorID.pt_BR.fl_str_mv
dc.contributor.advisorID.pt_BR.fl_str_mv
dc.contributor.referees1.none.fl_str_mv Santos, Araken de Medeiros
dc.contributor.referees1ID.pt_BR.fl_str_mv
dc.contributor.referees2.none.fl_str_mv Abreu, Marjory Cristiany da Costa
dc.contributor.referees2ID.pt_BR.fl_str_mv
dc.contributor.author.fl_str_mv Alves, Ystallonne Carlos da Silva
dc.contributor.advisor1.fl_str_mv Carvalho, Bruno Motta de
contributor_str_mv Carvalho, Bruno Motta de
dc.subject.cnpq.fl_str_mv CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
topic CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
Quantum computing
Super-resolution
Imagery enhancement
dc.subject.por.fl_str_mv Quantum computing
Super-resolution
Imagery enhancement
description Super-Resolution (SR) is a technique that has been exhaustively exploited and incorporates strategic aspects to image processing. As quantum computers gradually evolve and provide unconditional proof of a computational advantage at solving intractable problems over their classical counterparts, quantum computing emerges with the compelling argument of offering exponential speed-up to process computationally expensive operations. Envisioning the design of parallel, quantum-ready algorithms for near-term noisy devices and igniting Rapid and Accurate Image Super Resolution (RAISR), an implementation applying variational quantum computation is demonstrated for enhancing degraded imagery. This study proposes an approach that combines the benefits of RAISR, a non hallucinating and computationally efficient method, and Variational Quantum Eigensolver (VQE), a hybrid classical-quantum algorithm, to conduct SR with the support of a quantum computer, while preserving quantitative performance in terms of Image Quality Assessment (IQA). It covers the generation of additional hash-based filters learned with the classical implementation of the SR technique, in order to further explore performance improvements, produce images that are significantly sharper, and induce the learning of more powerful upscaling filters with integrated enhancement effects. As a result, it extends the potential of applying RAISR to improve low quality assets generated by low cost cameras, as well as fosters the eventual implementation of robust image enhancement methods powered by the use of quantum computation.
publishDate 2019
dc.date.accessioned.fl_str_mv 2019-12-04T22:14:36Z
dc.date.available.fl_str_mv 2019-12-04T22:14:36Z
dc.date.issued.fl_str_mv 2019-07-31
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.citation.fl_str_mv ALVES, Ystallonne Carlos da Silva. Quantum computing application in super-resolution. 2019. 106f. Dissertação (Mestrado em Sistemas e Computação) - Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2019.
dc.identifier.uri.fl_str_mv https://repositorio.ufrn.br/jspui/handle/123456789/28123
identifier_str_mv ALVES, Ystallonne Carlos da Silva. Quantum computing application in super-resolution. 2019. 106f. Dissertação (Mestrado em Sistemas e Computação) - Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2019.
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