CrossNet: An End-to-end Reference-based Super
Resolution Network using Cross-scale Warping
Abstract. 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 (8×). 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 fullyconvolutional 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 [1, 2] both in precision (around 2dB-4dB) and efficiency
(more than 100 times faster).