Image Super Resolution Using Sparse Image and Singular Values as Priors

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dc.contributor.author Ravishanka, Subrahmanyam
dc.contributor.author Reddy, Nagadastagiri Challapalle
dc.contributor.author Tripathi, Shikha
dc.contributor.author Murthy, K.V.V.
dc.contributor.editor Berciano, A.
dc.contributor.editor Díaz-Pernil, D.
dc.contributor.editor Kropatsch, W.
dc.contributor.editor Molina-Abril, H.
dc.contributor.editor Real, P.
dc.date.accessioned 2014-03-19T17:46:17Z
dc.date.available 2014-03-19T17:46:17Z
dc.date.issued 2011
dc.identifier.citation Murthy, K. V. V. et al., “Image super resolution using sparse image and singular values as priors”, in Computer Analysis of Images and Patterns, Springer Berlin Heidelberg, DOI: 10.1007/978-3-642-23678-5_45, 2011, pp. 380–388, ISBN: 978-3-642-23677-8. en_US
dc.identifier.isbn 9783642236778
dc.identifier.uri http://dx.doi.org/10.1007/978-3-642-23678-5_45
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/942
dc.description.abstract In this paper single image superresolution problem using sparse data representation is described. Image super-resolution is ill -posed inverse problem. Several methods have been proposed in the literature starting from simple interpolation techniques to learning based approach and under various regularization frame work. Recently many researchers have shown interest to super-resolve the image using sparse image representation. We slightly modified the procedure described by a similar work proposed recently. The modification suggested in the proposed approach is the method of dictionary training, feature extraction from the trained data base images and regularization. We have used singular values as prior for regularizing the ill-posed nature of the single image superresolution problem. Method of Optimal Directions algorithm (MOD) has been used in the proposed algorithm for obtaining high resolution and low resolution dictionaries from training image patches. Using the two dictionaries the given low resolution input image is super-resolved. The results of the proposed algorithm showed improvements in visual, PSNR, RMSE and SSIM metrics over other similar methods. en_US
dc.description.statementofresponsibility K. V. V. Murthy et al.,
dc.format.extent pp. 380–388
dc.language.iso en en_US
dc.publisher Springer en_US
dc.relation.ispartofseries Lecture Notes in Computer Science;Vol. 6855
dc.subject Method of Optimal Directions en_US
dc.subject Orthogonal Matching Pursuit en_US
dc.subject Singular Value Decomposition en_US
dc.subject Sparse representation en_US
dc.subject.ddc 006.42
dc.title Image Super Resolution Using Sparse Image and Singular Values as Priors en_US
dc.type Book chapter en_US


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