CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping
This addresses the issue of inter-patch misalignment and inefficiency in RefSR for image processing applications, representing a novel method rather than an incremental improvement.
The paper tackles the problem of reference-based super-resolution (RefSR) by proposing CrossNet, an end-to-end network using cross-scale warping, which improves precision by around 2dB-4dB and efficiency by more than 100 times compared to existing methods.
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).