Multi-Dimension Fusion Network for Light Field Spatial Super-Resolution using Dynamic Filters

• URL: http://arxiv.org/abs/2008.11449v1
• Date: Wed, 26 Aug 2020 09:05:07 GMT
• Title: Multi-Dimension Fusion Network for Light Field Spatial Super-Resolution using Dynamic Filters
• Authors: Qingyan Sun, Shuo Zhang, Song Chang, Lixi Zhu and Youfang Lin
• Abstract summary: We introduce a novel learning-based framework to improve the spatial resolution of light fields. Our reconstructed images also show sharp details and distinct lines in both sub-aperture images and epipolar plane images.
• Score: 23.82780431526054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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