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- W2736701133 abstract "In most of the existing regression-based methods, mapping matrices are directly learnt from features which are extracted from the interpolation results of low-resolution (LR) images. Nevertheless, this kind of features usually suffer from many artifacts which may produce bad effects on image super-resolution (SR) reconstruction. In this paper, we propose an effective single image super-resolution (SISR) method, which restores a high-resolution (HR) image through the feature enhancement mapping matrices and detail enhancement mapping matrices. The method bases on adjusted anchored neighborhood regression to get more accurate features and better mapping matrices. In the proposed framework, we train the linear mapping matrices twice. In the first stage, we extract interpolated LR image features and HR image features to learn feature enhancement mapping matrices, then these learnt matrices are used to enhance the extracted features. In the second stage, we learn detail enhancement mapping matrices from enhanced LR image features and HR image patches. With the two-stage learning strategy, more accurate mapping matrices can be obtained and thus better SR results can be achieved. Experimental results verify the effectiveness of our method compared to other state-of-the-art methods." @default.
- W2736701133 created "2017-07-31" @default.
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- W2736701133 date "2017-06-01" @default.
- W2736701133 modified "2023-09-25" @default.
- W2736701133 title "Single image super resolution based on feature enhancement" @default.
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- W2736701133 doi "https://doi.org/10.1109/icivc.2017.7984601" @default.
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