论文标题
使用动态过滤器的光场空间超级分辨率的多维融合网络
Multi-Dimension Fusion Network for Light Field Spatial Super-Resolution using Dynamic Filters
论文作者
论文摘要
事实证明，光场摄像机是3D重建和虚拟现实应用程序的强大工具。但是，光场图像的有限分辨率带来了许多困难，以进一步显示和提取。在本文中，我们介绍了一个基于学习的新型框架，以改善光场的空间分辨率。首先，来自不同维度的特征是在我们的多维融合体系结构中提取并融合在一起。然后，这些功能用于生成动态过滤器，这些过滤器从微镜图像中提取子像素信息，并隐式考虑差异信息。最后，在剩余分支中学习的更多高频细节被添加到上采样的图像中，并获得了最终的超级分辨光场。实验结果表明，所提出的方法使用的参数较少，但比各种数据集中其他最先进的方法更能实现更好的性能。我们的重建图像还显示了亚孔隙图像和外侧平面图像中的鲜明细节和不同的线条。
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.
