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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Author 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 associate professor at KLECET engineering colleges. After he is pursuing Doctoral Fellowship (PDF) from Lincoln University College, Malaysia.
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786202803175
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Zustand: 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. Bestandsnummer des Verkäufers 406092220
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 52 pp. Englisch. Bestandsnummer des Verkäufers 9786202803175
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786202803175
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Taschenbuch. Zustand: Neu. 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 | Jagannath Jadhav (u. a.) | Taschenbuch | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786202803175 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 119053332
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