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- W4308236210 abstract "Many light field image super-resolution methods aim to improve the quality of light-field image super-resolution reconstruction by exploiting the complementary information between sub-aperture images. Although these methods have achieved good results, the mutual information learning strategies between sub-aperture images are mostly handcrafted, limiting the super-resolution method to deal with the reconstruction of different scenes. We design a Transformer-based network named Multi-granularity Aggregation Transformer (MAT) to dynamically learn the complementary information between sub-aperture images in this paper. MAT is mainly implemented with the proposed multi-granularity aggregation blocks, which process sub-aperture images with three different granularity aggregation approaches and generate comprehensive spatial-angular representations for light field image super-resolution reconstruction. Extensive experiments are carried out on the mainstream light field image super-resolution datasets. MAT achieves new state-of-the-art results compared with other light field image super-resolution methods." @default.
- W4308236210 created "2022-11-09" @default.
- W4308236210 creator A5033462059 @default.
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- W4308236210 date "2022-10-16" @default.
- W4308236210 modified "2023-10-14" @default.
- W4308236210 title "Multi-Granularity Aggregation Transformer for Light Field Image Super-Resolution" @default.
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- W4308236210 doi "https://doi.org/10.1109/icip46576.2022.9898027" @default.
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