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- W4313463879 abstract "In this paper, we propose a residual dense networks (RDN)-based image super-resolution method using Meta-learning. Specifically, deep extraction of global features is performed on the external dataset through an RDN, meta-learning to obtain an initial parameter definitely for internal learning, so we can utilize both external and internal data. Our method achieves good results with only one gradient update. And it can be appropriate for image super-resolution under the action of different blur kernels, with a wider application range and high flexibility." @default.
- W4313463879 created "2023-01-06" @default.
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- W4313463879 date "2022-11-03" @default.
- W4313463879 modified "2023-09-30" @default.
- W4313463879 title "AN RDN-based image super-resolution method using Meta-learning" @default.
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- W4313463879 doi "https://doi.org/10.1145/3571662.3571673" @default.
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