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• W2740172070 abstract "Since the single image super-resolution (SR) is an extremely ill posed problem, we introduce a novel auto-regressive moving average (ARMA) model-based regularization term into the spare representation-based framework to deal with it in this paper. In our framework, we have a dual regularization. Firstly, we use the ARMA models trained from external samples to establish a regularization term. ARMA model-based regularization serves as a local constraint. Secondly, we introduce the nonlocal (NL) self-similarity as another regularization term. Both the local and the NL regularizations are unified into the sparse representation-based framework. Finally, extensive experiments verify the effectiveness of the proposed method." @default.
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• W2740172070 date "2017-12-01" @default.
• W2740172070 modified "2023-09-23" @default.
• W2740172070 title "Image sparse representation with local ARMA and nonlocal self-similarity regularizations for super-resolution" @default.
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• W2740172070 doi "https://doi.org/10.1016/j.optcom.2017.07.031" @default.
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