# MRI Super-Resolution with Ensemble Learning and Complementary Priors

arXiv: https://arxiv.org/abs/1907.03063 · HTML: https://mlsanity.com/paper/1907.03063/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2019.

## Built on by (most cited first, 27 of 27)

- [Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models](https://mlsanity.com/paper/2209.12104/) (2022)
- [Task Transformer Network for Joint MRI Reconstruction and Super-Resolution](https://mlsanity.com/paper/2106.06742/) (2021)
- [IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representation](https://mlsanity.com/paper/2106.15097/) (2021)
- [Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution](https://mlsanity.com/paper/2203.13963/) (2022)
- [High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss](https://mlsanity.com/paper/2107.09989/) (2021)
- [TransMRSR: Transformer-based Self-Distilled Generative Prior for Brain MRI Super-Resolution](https://mlsanity.com/paper/2306.06669/) (2023)
- [Brain Graph Super-Resolution Using Adversarial Graph Neural Network with Application to Functional Brain Connectivity](https://mlsanity.com/paper/2105.00425/) (2021)
- [MRI Super-Resolution with Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2511.16854/) (2025)
- [Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution](https://mlsanity.com/paper/2103.02940/) (2021)
- [ECLARE: Efficient cross-planar learning for anisotropic resolution enhancement](https://mlsanity.com/paper/2503.11787/) (2025)
- [Energy-Guided Diffusion Model for CBCT-to-CT Synthesis](https://mlsanity.com/paper/2308.03354/) (2023)
- [Single Image Super-Resolution Methods: A Survey](https://mlsanity.com/paper/2202.11763/) (2022)
- [Generalized super-resolution 4D Flow MRI unicodex2013 using ensemble learning to extend across the cardiovascular system](https://mlsanity.com/paper/2311.11819/) (2023)
- [Super-resolution of biomedical volumes with 2D supervision](https://mlsanity.com/paper/2404.09425/) (2024)
- [CAIR: Fast and Lightweight Multi-Scale Color Attention Network for Instagram Filter Removal](https://mlsanity.com/paper/2208.14039/) (2022)
- [An Unsupervised Framework for Joint MRI Super Resolution and Gibbs Artifact Removal](https://mlsanity.com/paper/2302.02849/) (2023)
- [Fast Medical Shape Reconstruction via Meta-learned Implicit Neural Representations](https://mlsanity.com/paper/2409.07100/) (2024)
- [SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging](https://mlsanity.com/paper/2104.05901/) (2021)
- [Generative AI for Rapid Diffusion MRI with Improved Image Quality, Reliability and Generalizability](https://mlsanity.com/paper/2303.05686/) (2023)
- [Joint MR sequence optimization beats pure neural network approaches for spin-echo MRI super-resolution](https://mlsanity.com/paper/2305.07524/) (2023)
- [Enhance the Image: Super Resolution using Artificial Intelligence in MRI](https://mlsanity.com/paper/2406.13625/) (2024)
- [Learning Two-factor Representation for Magnetic Resonance Image Super-resolution](https://mlsanity.com/paper/2409.09731/) (2024)
- [EnsIR: An Ensemble Algorithm for Image Restoration via Gaussian Mixture Models](https://mlsanity.com/paper/2410.22959/) (2024)
- [A transformer-based deep learning approach for classifying brain metastases into primary organ sites using clinical whole brain MRI](https://mlsanity.com/paper/2110.03588/) (2021)
- [Flexible Alignment Super-Resolution Network for Multi-Contrast MRI](https://mlsanity.com/paper/2210.03460/) (2022)
- [Rapid Whole Brain Motion-robust Mesoscale In-vivo MR Imaging using Multi-scale Implicit Neural Representation](https://mlsanity.com/paper/2502.08634/) (2025)
- [CD-DPE: Dual-Prompt Expert Network Based on Convolutional Dictionary Feature Decoupling for Multi-Contrast MRI Super-Resolution](https://mlsanity.com/paper/2511.14014/) (2025)

## Abstract (arXiv)

Magnetic resonance imaging (MRI) is a widely used medical imaging modality. However, due to the limitations in hardware, scan time, and throughput, it is often clinically challenging to obtain high-quality MR images. The super-resolution approach is potentially promising to improve MR image quality without any hardware upgrade. In this paper, we propose an ensemble learning and deep learning framework for MR image super-resolution. In our study, we first enlarged low resolution images using 5 commonly used super-resolution algorithms and obtained differentially enlarged image datasets with complementary priors. Then, a generative adversarial network (GAN) is trained with each dataset to generate super-resolution MR images. Finally, a convolutional neural network is used for ensemble learning that synergizes the outputs of GANs into the final MR super-resolution images. According to our results, the ensemble learning results outcome any one of GAN outputs. Compared with some state-of-the-art deep learning-based super-resolution methods, our approach is advantageous in suppressing artifacts and keeping more image details.