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2015 | OriginalPaper | Buchkapitel

Edge Directed Single Image Super Resolution Through the Learning Based Gradient Regression Estimation

verfasst von : Dandan Si, Yuanyuan Hu, Zongliang Gan, Ziguan Cui, Feng Liu

Erschienen in: Image and Graphics

Verlag: Springer International Publishing

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Abstract

Single image super resolution (SR) aims to estimate high resolution (HR) image from the low resolution (LR) one, and estimating accuracy of HR image gradient is very important for edge directed image SR methods. In this paper, we propose a novel edge directed image SR method by learning based gradient estimation. In proposed method, the gradient of HR image is estimated by using the example based ridge regression model. Recognizing that the training samples of the given sub-set for regression should have similar local geometric structure based on clustering, we employ high frequency of LR image patches with removing the mean value to perform such clustering. Moreover, the precomputed projective matrix of the ridge regression can reduce the computational complexity further. Experimental results suggest that the proposed method can achieve better gradient estimation of HR image and competitive SR quality compared with other SR methods.

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Titel: Edge Directed Single Image Super Resolution Through the Learning Based Gradient Regression Estimation
verfasst von: Dandan Si
Yuanyuan Hu
Zongliang Gan
Ziguan Cui
Feng Liu
Verlag: Springer International Publishing
Buch: Image and Graphics
Print ISBN: 978-3-319-21962-2

Electronic ISBN: 978-3-319-21963-9

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-21963-9_21

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