# Cross-Modality High-Frequency Transformer for MR Image Super-Resolution

arXiv: https://arxiv.org/abs/2203.15314 · HTML: https://mlsanity.com/paper/2203.15314/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 49 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [Segmenter: Transformer for Semantic Segmentation](https://mlsanity.com/paper/2105.05633/) (2021)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [Learning Texture Transformer Network for Image Super-Resolution](https://mlsanity.com/paper/2006.04139/) (2020)
- [Single Image Super-Resolution via a Holistic Attention Network](https://mlsanity.com/paper/2008.08767/) (2020)
- [Conformer: Local Features Coupling Global Representations for Visual Recognition](https://mlsanity.com/paper/2105.03889/) (2021)
- [Non-Local Recurrent Network for Image Restoration](https://mlsanity.com/paper/1806.02919/) (2018)
- [Structure-Preserving Super Resolution with Gradient Guidance](https://mlsanity.com/paper/2003.13081/) (2020)
- [Cross-Modality Fusion Transformer for Multispectral Object Detection](https://mlsanity.com/paper/2111.00273/) (2021)
- [MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution](https://mlsanity.com/paper/2106.02299/) (2021)
- [Task Transformer Network for Joint MRI Reconstruction and Super-Resolution](https://mlsanity.com/paper/2106.06742/) (2021)
- [Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks](https://mlsanity.com/paper/1801.02728/) (2018)
- [Multi-Contrast MRI Super-Resolution via a Multi-Stage Integration Network](https://mlsanity.com/paper/2105.08949/) (2021)
- [Image-and-Spatial Transformer Networks for Structure-Guided Image Registration](https://mlsanity.com/paper/1907.09200/) (2019)
- [Channel Splitting Network for Single MR Image Super-Resolution](https://mlsanity.com/paper/1810.06453/) (2018)
- [Cross-Modality Deep Feature Learning for Brain Tumor Segmentation](https://mlsanity.com/paper/2201.02356/) (2022)
- [Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution](https://mlsanity.com/paper/2005.00953/) (2020)
- [CMTR: Cross-modality Transformer for Visible-infrared Person Re-identification](https://mlsanity.com/paper/2110.08994/) (2021)
- [Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors](https://mlsanity.com/paper/1909.04572/) (2019)

## Built on by (most cited first, 20 of 20)

- [Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review](https://mlsanity.com/paper/2301.03505/) (2023)
- [Transformers in Healthcare: A Survey](https://mlsanity.com/paper/2307.00067/) (2023)
- [CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data](https://mlsanity.com/paper/2404.04878/) (2024)
- [Re-Attention Transformer for Weakly Supervised Object Localization](https://mlsanity.com/paper/2208.01838/) (2022)
- [RViDeformer: Efficient Raw Video Denoising Transformer with a Larger Benchmark Dataset](https://mlsanity.com/paper/2305.00767/) (2023)
- [SASFormer: Transformers for Sparsely Annotated Semantic Segmentation](https://mlsanity.com/paper/2212.02019/) (2022)
- [Deep Unfolding Convolutional Dictionary Model for Multi-Contrast MRI Super-resolution and Reconstruction](https://mlsanity.com/paper/2309.01171/) (2023)
- [A Hybrid Transformer-Mamba Network for Single Image Deraining](https://mlsanity.com/paper/2409.00410/) (2024)
- [Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution](https://mlsanity.com/paper/2504.10105/) (2025)
- [Fourier-RWKV: A Multi-State Perception Network for Efficient Image Dehazing](https://mlsanity.com/paper/2512.08161/) (2025)
- [Parameterization of Cross-Token Relations with Relative Positional Encoding for Vision MLP](https://mlsanity.com/paper/2207.07284/) (2022)
- [Enhance the Image: Super Resolution using Artificial Intelligence in MRI](https://mlsanity.com/paper/2406.13625/) (2024)
- [Dual-domain Adaptation Networks for Realistic Image Super-resolution](https://mlsanity.com/paper/2511.17217/) (2025)
- [Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement](https://mlsanity.com/paper/2304.12130/) (2023)
- [DeepLight: Reconstructing High-Resolution Observations of Nighttime Light With Multi-Modal Remote Sensing Data](https://mlsanity.com/paper/2402.15659/) (2024)
- [Modeling Continuous Spatial-temporal Dynamics of Turbulent Flow with Test-time Refinement](https://mlsanity.com/paper/2412.19927/) (2024)
- [Decoupling Multi-Contrast Super-Resolution: Self-Supervised Implicit Re-Representation for Unpaired Cross-Modal Synthesis](https://mlsanity.com/paper/2505.05855/) (2025)
- [BrainPuzzle: Hybrid Physics and Data-Driven Reconstruction for Transcranial Ultrasound Tomography](https://mlsanity.com/paper/2510.20029/) (2025)
- [Versatile and Efficient Medical Image Super-Resolution Via Frequency-Gated Mamba](https://mlsanity.com/paper/2510.27296/) (2025)
- [Tissue-Mixture Entropy-Weighted Reconstruction for Partial-Volume-Aware Brain MRI Super-Resolution](https://mlsanity.com/paper/2608.26647/) (2026)

## Abstract (arXiv)

Improving the resolution of magnetic resonance (MR) image data is critical to computer-aided diagnosis and brain function analysis. Higher resolution helps to capture more detailed content, but typically induces to lower signal-to-noise ratio and longer scanning time. To this end, MR image super-resolution has become a widely-interested topic in recent times. Existing works establish extensive deep models with the conventional architectures based on convolutional neural networks (CNN). In this work, to further advance this research field, we make an early effort to build a Transformer-based MR image super-resolution framework, with careful designs on exploring valuable domain prior knowledge. Specifically, we consider two-fold domain priors including the high-frequency structure prior and the inter-modality context prior, and establish a novel Transformer architecture, called Cross-modality high-frequency Transformer (Cohf-T), to introduce such priors into super-resolving the low-resolution (LR) MR images. Experiments on two datasets indicate that Cohf-T achieves new state-of-the-art performance.