  The Reference-based Super-resolution (RefSR) super-resolves a low-resolution
(LR) image given an external high-resolution (HR) reference image, where the
reference image and LR image share similar viewpoint but with significant
resolution gap x8. Existing RefSR methods work in a cascaded way such as patch
matching followed by synthesis pipeline with two independently defined
objective functions, leading to the inter-patch misalignment, grid effect and
inefficient optimization. To resolve these issues, we present CrossNet, an
end-to-end and fully-convolutional deep neural network using cross-scale
warping. Our network contains image encoders, cross-scale warping layers, and
fusion decoder: the encoder serves to extract multi-scale features from both
the LR and the reference images; the cross-scale warping layers spatially
aligns the reference feature map with the LR feature map; the decoder finally
aggregates feature maps from both domains to synthesize the HR output. Using
cross-scale warping, our network is able to perform spatial alignment at
pixel-level in an end-to-end fashion, which improves the existing schemes both
in precision (around 2dB-4dB) and efficiency (more than 100 times faster).
