Quantum computing application in super-resolution
Autor(a) principal: | |
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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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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 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
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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https://repositorio.ufrn.br/jspui/handle/123456789/28123 |
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