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- W2726304302 abstract "The external example-driven single image super-resolution (SISR) method that uses a deep convolutional neural network (CNN) has exhibited superior performance as compared to previously developed SISR methods. However, the advantages of jointly using external and internal examples on a deep CNN framework have not been sufficiently investigated. In this letter, we present a novel method for single image super-resolution by exploiting a complementary relation between external and internal example-based SISR methods. The proposed deep CNN model consists of two subnetworks, a global residual network and a self-residual network, to utilize the advantages of both external and internal examples. In contrast with conventional joint SISR methods, the proposed method is the first deep CNN-based SISR method that does not require a retraining process, which tends to be inefficient. The proposed method outperformed existing methods in both quantitative and qualitative evaluations." @default.
- W2726304302 created "2017-07-14" @default.
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- W2726304302 date "2017-08-01" @default.
- W2726304302 modified "2023-09-26" @default.
- W2726304302 title "Deep CNN-Based Super-Resolution Using External and Internal Examples" @default.
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- W2726304302 doi "https://doi.org/10.1109/lsp.2017.2721104" @default.
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