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

arXiv: https://arxiv.org/abs/1807.10547 · HTML: https://mlsanity.com/paper/1807.10547/

## Key facts

- Cited by 91 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2018.

## Built on by (most cited first, 30 of 91)

- [Exploiting Diffusion Prior for Real-World Image Super-Resolution](https://mlsanity.com/paper/2305.07015/) (2023)
- [Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors](https://mlsanity.com/paper/2202.13142/) (2022)
- [MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution](https://mlsanity.com/paper/2106.02299/) (2021)
- [Any-resolution Training for High-resolution Image Synthesis](https://mlsanity.com/paper/2204.07156/) (2022)
- [Robust Reference-based Super-Resolution via C2-Matching](https://mlsanity.com/paper/2106.01863/) (2021)
- [CoSeR: Bridging Image and Language for Cognitive Super-Resolution](https://mlsanity.com/paper/2311.16512/) (2023)
- [Reference-based Image Super-Resolution with Deformable Attention Transformer](https://mlsanity.com/paper/2207.11938/) (2022)
- [HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance](https://mlsanity.com/paper/2307.07829/) (2023)
- [Dual-Camera Super-Resolution with Aligned Attention Modules](https://mlsanity.com/paper/2109.01349/) (2021)
- [LocalTrans: A Multiscale Local Transformer Network for Cross-Resolution Homography Estimation](https://mlsanity.com/paper/2106.04067/) (2021)
- [Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations](https://mlsanity.com/paper/2203.01325/) (2022)
- [Reference-based Video Super-Resolution Using Multi-Camera Video Triplets](https://mlsanity.com/paper/2203.14537/) (2022)
- [A Systematic Survey of Deep Learning-based Single-Image Super-Resolution](https://mlsanity.com/paper/2109.14335/) (2021)
- [Coarse-to-Fine Embedded PatchMatch and Multi-Scale Dynamic Aggregation for Reference-based Super-Resolution](https://mlsanity.com/paper/2201.04358/) (2022)
- [GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond](https://mlsanity.com/paper/2207.14812/) (2022)
- [Variational AutoEncoder for Reference based Image Super-Resolution](https://mlsanity.com/paper/2106.04090/) (2021)
- [Revisiting Light Field Rendering with Deep Anti-Aliasing Neural Network](https://mlsanity.com/paper/2104.06797/) (2021)
- [Hyperspectral Image Super Resolution with Real Unaligned RGB Guidance](https://mlsanity.com/paper/2302.06298/) (2023)
- [Attention-based Multi-Reference Learning for Image Super-Resolution](https://mlsanity.com/paper/2108.13697/) (2021)
- [RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection](https://mlsanity.com/paper/2211.04203/) (2022)
- [LMR: A Large-Scale Multi-Reference Dataset for Reference-based Super-Resolution](https://mlsanity.com/paper/2303.04970/) (2023)
- [RZSR: Reference-based Zero-Shot Super-Resolution with Depth Guided Self-Exemplars](https://mlsanity.com/paper/2208.11313/) (2022)
- [Reference-based Image and Video Super-Resolution via C2-Matching](https://mlsanity.com/paper/2212.09581/) (2022)
- [Blind Image Super-resolution with Rich Texture-Aware Codebooks](https://mlsanity.com/paper/2310.17188/) (2023)
- [EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation](https://mlsanity.com/paper/2110.07797/) (2021)
- [Neural Volume Super-Resolution](https://mlsanity.com/paper/2212.04666/) (2022)
- [TransRef: Multi-Scale Reference Embedding Transformer for Reference-Guided Image Inpainting](https://mlsanity.com/paper/2306.11528/) (2023)
- [Training-Free Large Model Priors for Multiple-in-One Image Restoration](https://mlsanity.com/paper/2407.13181/) (2024)
- [Degradation-Modeled Multipath Diffusion for Tunable Metalens Photography](https://mlsanity.com/paper/2506.22753/) (2025)
- [Light Field Reconstruction via Deep Adaptive Fusion of Hybrid Lenses](https://mlsanity.com/paper/2102.07085/) (2021)

## Abstract (arXiv)

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).