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• W2802679327 abstract "Owing to the ill-posed nature of the image super-resolution (SR) problem, learning-based approaches typically employ regularization terms in the representation. Current local-patch based face SR approaches weight representation coefficients to obtain adaptive and accurate priors. However, they ignore the fact that heteroskedasticity generally exists both in the observed data and representation coefficients. In this paper, we present a novel adaptive and weighted representation framework for face SR to further exploit adaptive and accurate prior information for different content inputs. Moreover, we enrich patch priors by sampling context patches, and learn the residual high-frequency components for better reconstruction performance. Experiments on the CAS-PEAL-R1 face database show that our proposed approach outperforms state-of-the-arts that include other deep learning based methods." @default.
• W2802679327 created "2018-05-17" @default.
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• W2802679327 date "2018-01-01" @default.
• W2802679327 modified "2023-09-27" @default.
• W2802679327 title "AWCR: Adaptive and Weighted Collaborative Representations for Face Super-Resolution with Context Residual-Learning" @default.
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• W2802679327 doi "https://doi.org/10.1007/978-3-319-77380-3_11" @default.
• W2802679327 hasPublicationYear "2018" @default.
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