  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.
