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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 - Couverture souple
Synopsis
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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- ÉditeurLAP LAMBERT Academic Publishing
- Date d'édition2020
- ISBN 10 6202803177
- ISBN 13 9786202803175
- ReliureBroché
- Langueanglais
- Nombre de pages52
- Coordonnées du fabricantnon disponible
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Sparse Signal Representation for a single-image Super Resolution
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6202803177
ISBN 13 : 9786202803175
Neuf
Couverture souple
Vendeur : moluna, Greven, Allemagne
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Etat : 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. N° de réf. du vendeur 406092220
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Sparse Signal Representation for a single-image Super Resolution
Edité par
LAP LAMBERT Academic Publishing Sep 2020, 2020
ISBN 10 : 6202803177
ISBN 13 : 9786202803175
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : 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. N° de réf. du vendeur 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
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6202803177
ISBN 13 : 9786202803175
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : 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. N° de réf. du vendeur 9786202803175
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Sparse Signal Representation for a single-image Super Resolution
Edité par
LAP LAMBERT Academic Publishing Sep 2020, 2020
ISBN 10 : 6202803177
ISBN 13 : 9786202803175
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : 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. N° de réf. du vendeur 9786202803175
Quantité disponible : 2 disponible(s)