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- W3204332511 abstract "The goal of the stereoscopic image super-resolution (SR) is to reconstruct a pair of high-resolution (HR) images from corresponding low-resolution (LR) images. The existing stereo SR methods based on convolutional neural network (CNN) benefit from additional information from a different viewpoint to some extent. However, they cannot make good use of the complementary information from the different viewpoint, resulting in a lack of textures and details. The unevenly distributed features from left and right images were treated equally. To overcome the above difficulties, we put forward a stereo attention graph neural network (SA-GNN), which can extract reliable priors non-locally and fuse consistent contents adaptively cross different viewpoints. SA-GNN contains a series of stereo graph neural networks (SGNN), which alternate iteratively between in-view graph and cross-view graph under the aggregation and update mechanism of graph neural networks (GNN) to enhance SR performance. The comparison experiment results on four public datasets demonstrate that our SA-GNN outperforms the state-of-the-art methods." @default.
- W3204332511 created "2021-10-11" @default.
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- W3204332511 date "2021-01-01" @default.
- W3204332511 modified "2023-10-16" @default.
- W3204332511 title "SA-GNN: Stereo Attention and Graph Neural Network for Stereo Image Super-Resolution" @default.
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- W3204332511 doi "https://doi.org/10.1007/978-3-030-87361-5_33" @default.
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