Spatial-Angular Interaction for Light Field Image Super-Resolution
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Light field (LF) cameras record both intensity and directions of light rays, and capture scenes from a number of viewpoints. Both information within each perspective (i.e., spatial information) and among different perspectives (i.e., angular information) is beneficial to image super-resolution (SR). In this paper, we propose a spatial-angular interactive network (namely, LF-InterNet) for LF image SR. In our method, spatial and angular features are separately extracted from the input LF using two specifically designed convolutions. These extracted features are then repetitively interacted to incorporate both spatial and angular information. Finally, the interacted spatial and angular features are fused to super-resolve each sub-aperture image. Experiments on 6 public LF datasets have demonstrated the superiority of our method. As compared to existing LF and single image SR methods, our method can recover much more details, and achieves significant improvements over the state-of-the-arts in terms of PSNR and SSIM.
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