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Sparse Signal Representation for a single-image Super Resolution: The research on image Statistics gives a forward step to represent the image patch in a better way - Brossura
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Many effective approaches designed to solve ill-posed and ill-conditioned problem had deficiencies to fulfill the needs of point spread function (PSF), which is hard to get into the practical situation all the time. So this project introduces a method called as Sparse signal representation for a single-image Super Resolution. The research on image Statistics gives a forward step to represent the image patches in a better way, as a sparse linear combination of elements, which are chosen from complete dictionary. From the coefficients of the sparse representation are utilized to construct the high-resolution output image. Here it trains two dictionaries jointly for the low-and high-resolution image patch, which produces two individual dictionary and it shows that the sparse representations for low- and high-resolution is same. To produce a high resolution image patch, the sparse representation can put together two trained dictionaries of the low- and the high-resolution image patch. A large amount of image patch pair are sampled here, by decreasing the computational cost significantly.
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- EditoreLAP LAMBERT Academic Publishing
- Data di pubblicazione2020
- ISBN 10 6202803177
- ISBN 13 9786202803175
- RilegaturaCopertina flessibile
- LinguaInglese
- Numero di pagine52
- Contatto del produttorenon disponibile
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Sparse Signal Representation for a single-image Super Resolution
Editore:
LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202803177
ISBN 13: 9786202803175
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Brossura
Da: moluna, Greven, Germania
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Jadhav Dr. JagannathAuthor is a Renowned Researcher & Scientist Global with Patents in his name. He is an Academician, researcher, author, writer, inventor and innovator, Scientist (Consultant and speaker). Having experience as assoc. Codice articolo 406092220
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Sparse Signal Representation for a single-image Super Resolution
Editore:
LAP LAMBERT Academic Publishing Sep 2020, 2020
ISBN 10: 6202803177
ISBN 13: 9786202803175
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Taschenbuch
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Many effective approaches designed to solve ill-posed and ill-conditioned problem had deficiencies to fulfill the needs of point spread function (PSF), which is hard to get into the practical situation all the time. So this project introduces a method called as Sparse signal representation for a single-image Super Resolution. The research on image Statistics gives a forward step to represent the image patches in a better way, as a sparse linear combination of elements, which are chosen from complete dictionary. From the coefficients of the sparse representation are utilized to construct the high-resolution output image. Here it trains two dictionaries jointly for the low-and high-resolution image patch, which produces two individual dictionary and it shows that the sparse representations for low- and high-resolution is same. To produce a high resolution image patch, the sparse representation can put together two trained dictionaries of the low- and the high-resolution image patch. A large amount of image patch pair are sampled here, by decreasing the computational cost significantly. 52 pp. Englisch. Codice articolo 9786202803175
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Sparse Signal Representation for a single-image Super Resolution
Editore:
LAP LAMBERT Academic Publishing Sep 2020, 2020
ISBN 10: 6202803177
ISBN 13: 9786202803175
Nuovo
Taschenbuch
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. Neuware -Many effective approaches designed to solve ill-posed and ill-conditioned problem had deficiencies to fulfill the needs of point spread function (PSF), which is hard to get into the practical situation all the time. So this project introduces a method called as Sparse signal representation for a single-image Super Resolution. The research on image Statistics gives a forward step to represent the image patches in a better way, as a sparse linear combination of elements, which are chosen from complete dictionary. From the coefficients of the sparse representation are utilized to construct the high-resolution output image. Here it trains two dictionaries jointly for the low-and high-resolution image patch, which produces two individual dictionary and it shows that the sparse representations for low- and high-resolution is same. To produce a high resolution image patch, the sparse representation can put together two trained dictionaries of the low- and the high-resolution image patch. A large amount of image patch pair are sampled here, by decreasing the computational cost significantly.Books on Demand GmbH, Überseering 33, 22297 Hamburg 52 pp. Englisch. Codice articolo 9786202803175
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Sparse Signal Representation for a single-image Super Resolution : The research on image Statistics gives a forward step to represent the image patch in a better way
Editore:
LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202803177
ISBN 13: 9786202803175
Nuovo
Taschenbuch
Da: AHA-BUCH GmbH, Einbeck, Germania
Valutazione del venditore 5 su 5 stelle
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Many effective approaches designed to solve ill-posed and ill-conditioned problem had deficiencies to fulfill the needs of point spread function (PSF), which is hard to get into the practical situation all the time. So this project introduces a method called as Sparse signal representation for a single-image Super Resolution. The research on image Statistics gives a forward step to represent the image patches in a better way, as a sparse linear combination of elements, which are chosen from complete dictionary. From the coefficients of the sparse representation are utilized to construct the high-resolution output image. Here it trains two dictionaries jointly for the low-and high-resolution image patch, which produces two individual dictionary and it shows that the sparse representations for low- and high-resolution is same. To produce a high resolution image patch, the sparse representation can put together two trained dictionaries of the low- and the high-resolution image patch. A large amount of image patch pair are sampled here, by decreasing the computational cost significantly. Codice articolo 9786202803175
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