  Recent learning-based approaches have achieved significant progress in light
field (LF) image super-resolution (SR) by exploring convolution-based or
transformer-based network structures. However, LF imaging has many intrinsic
physical priors that have not been fully exploited. In this paper, we analyze
the coordinate transformation of the LF imaging process to reveal the geometric
relationship in the LF images. Based on such geometric priors, we introduce a
new LF subspace of virtual-slit images (VSI) that provide sub-pixel information
complementary to sub-aperture images. To leverage the abundant correlation
across the four-dimensional data with manageable complexity, we propose
learning ensemble representation of all $C_4^2$ LF subspaces for more effective
feature extraction. To super-resolve image structures from undersampled LF
data, we propose a geometry-aware decoder, named EPIXformer, which constrains
the transformer's operational searching regions with a LF physical prior.
Experimental results on both spatial and angular SR tasks demonstrate that the
proposed method outperforms other state-of-the-art schemes, especially in
handling various disparities.
