Matches in SemOpenAlex for { <https://semopenalex.org/work/W4283271720> ?p ?o ?g. }
- W4283271720 abstract "Magnetic resonance imaging plays an important role in auxiliary diagnosis and brain exploration. However, limited by hardware, scanning time and cost, it’s challenging to acquire high-resolution (HR) magnetic resonance (MR) image clinically. In this paper, consistent feature generative adversarial network (CFGAN) is proposed to produce HR MR images from the low-resolution counterparts. Specifically, a consistent-features encoder is employed to extract the multi-scales features and encode them into latent codes. Then, a progressive generator is utilized to decode the latent codes from high-level to low-level features. With the encoder and generator, the shared consistent features between low-resolution and high-resolution can be fully extracted and recovered. Experiments on ADNI dataset demonstrate that CFGAN outperforms the competing methods quantitatively and qualitatively." @default.
- W4283271720 created "2022-06-23" @default.
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- W4283271720 date "2022-02-18" @default.
- W4283271720 modified "2023-09-27" @default.
- W4283271720 title "Brain MR Images Super-Resolution with the Consistent Features" @default.
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- W4283271720 doi "https://doi.org/10.1145/3529836.3529939" @default.
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