  Various combinations of cameras enrich computational photography, among which
reference-based superresolution (RefSR) plays a critical role in multiscale
imaging systems. However, existing RefSR approaches fail to accomplish
high-fidelity super-resolution under a large resolution gap, e.g., 8x
upscaling, due to the lower consideration of the underlying scene structure. In
this paper, we aim to solve the RefSR problem in actual multiscale camera
systems inspired by multiplane image (MPI) representation. Specifically, we
propose Cross-MPI, an end-to-end RefSR network composed of a novel plane-aware
attention-based MPI mechanism, a multiscale guided upsampling module as well as
a super-resolution (SR) synthesis and fusion module. Instead of using a direct
and exhaustive matching between the cross-scale stereo, the proposed
plane-aware attention mechanism fully utilizes the concealed scene structure
for efficient attention-based correspondence searching. Further combined with a
gentle coarse-to-fine guided upsampling strategy, the proposed Cross-MPI can
achieve a robust and accurate detail transmission. Experimental results on both
digitally synthesized and optical zoom cross-scale data show that the Cross-MPI
framework can achieve superior performance against the existing RefSR methods
and is a real fit for actual multiscale camera systems even with large-scale
differences.
