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- W3080316399 abstract "Light field cameras have been proved to be powerful tools for 3D reconstruction and virtual reality applications. However, the limited resolution of light field images brings a lot of difficulties for further information display and extraction. In this paper, we introduce a novel learning-based framework to improve the spatial resolution of light fields. First, features from different dimensions are parallelly extracted and fused together in our multi-dimension fusion architecture. These features are then used to generate dynamic filters, which extract subpixel information from micro-lens images and also implicitly consider the disparity information. Finally, more high-frequency details learned in the residual branch are added to the upsampled images and the final super-resolved light fields are obtained. Experimental results show that the proposed method uses fewer parameters but achieves better performances than other state-of-the-art methods in various kinds of datasets. Our reconstructed images also show sharp details and distinct lines in both sub-aperture images and epipolar plane images." @default.
- W3080316399 created "2020-09-01" @default.
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- W3080316399 date "2020-08-26" @default.
- W3080316399 modified "2023-09-27" @default.
- W3080316399 title "Multi-Dimension Fusion Network for Light Field Spatial Super-Resolution using Dynamic Filters" @default.
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- W3080316399 doi "https://doi.org/10.48550/arxiv.2008.11449" @default.
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