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- W4289242904 abstract "Convolutional neural networks have recently demonstrated interesting results for single image super-resolution. However, these networks were trained to deal with super-resolution problem on natural images. In this paper, we adapt a deep network, which was proposed for natural images superresolution, to single text image super-resolution. To evaluate the network, we present our database for single text image super-resolution. Moreover, we propose to combine Gradient Difference Loss (GDL) with L1/L2 loss to enhance edges in super-resolution image. Quantitative and qualitative evaluations on our dataset show that adding the GDL improves the super-resolution results." @default.
- W4289242904 created "2022-08-01" @default.
- W4289242904 creator A5038432329 @default.
- W4289242904 creator A5050102022 @default.
- W4289242904 date "2018-11-26" @default.
- W4289242904 modified "2023-09-29" @default.
- W4289242904 title "Deep Laplacian Pyramid Network for Text Images Super-Resolution" @default.
- W4289242904 doi "https://doi.org/10.48550/arxiv.1811.10449" @default.
- W4289242904 hasPublicationYear "2018" @default.
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