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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 - Tapa blanda

 
9786202803175: 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

Sinopsis

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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  • EditorialLAP LAMBERT Academic Publishing
  • Año de publicación2020
  • ISBN 10 6202803177
  • ISBN 13 9786202803175
  • EncuadernaciónTapa blanda
  • IdiomaInglés
  • Número de páginas52
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Jagannath Jadhav
ISBN 10: 6202803177 ISBN 13: 9786202803175
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Taschenbuch. Condición: 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. Nº de ref. del artículo: 9786202803175

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Jagannath Jadhav
Publicado por LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202803177 ISBN 13: 9786202803175
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Taschenbuch. Condición: 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. Nº de ref. del artículo: 9786202803175

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Dr. Jagannath Jadhav|Prof.Amruta Jadhav
Publicado por LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202803177 ISBN 13: 9786202803175
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Condición: 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. Nº de ref. del artículo: 406092220

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Taschenbuch. Condición: 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. Nº de ref. del artículo: 9786202803175

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