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

CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping