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

ISBN 10: 6202803177 / ISBN 13: 9786202803175

Edité par LAP LAMBERT Academic Publishing, 2020

A propos de cet article

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

Détails bibliographiques

• Titre: 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
• Éditeur: LAP LAMBERT Academic Publishing
• Date de parution: 2020
• Langue: anglais
• ISBN à 10 chiffres: 6202803177
• ISBN à 13 chiffres: 9786202803175
• Reliure: Taschenbuch
• État de l'article: Neu

A propos de ce titre

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.