Dual Path Cross-Scale Attention Network For Image inpainting

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.