# Medical Image Fusion: A survey of the state of the art

arXiv: https://arxiv.org/abs/1401.0166 · HTML: https://mlsanity.com/paper/1401.0166/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2013.

## Built on by (most cited first, 30 of 39)

- [CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion](https://mlsanity.com/paper/2211.14461/) (2022)
- [DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion](https://mlsanity.com/paper/2303.06840/) (2023)
- [Equivariant Multi-Modality Image Fusion](https://mlsanity.com/paper/2305.11443/) (2023)
- [MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images](https://mlsanity.com/paper/2207.07027/) (2022)
- [Image Fusion via Vision-Language Model](https://mlsanity.com/paper/2402.02235/) (2024)
- [MambaDFuse: A Mamba-based Dual-phase Model for Multi-modality Image Fusion](https://mlsanity.com/paper/2404.08406/) (2024)
- [Modality specific U-Net variants for biomedical image segmentation: A survey](https://mlsanity.com/paper/2107.04537/) (2021)
- [Multi-Modality Cardiac Image Computing: A Survey](https://mlsanity.com/paper/2208.12881/) (2022)
- [ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learning](https://mlsanity.com/paper/2312.07943/) (2023)
- [Dynamic Enhancement Network for Partial Multi-modality Person Re-identification](https://mlsanity.com/paper/2305.15762/) (2023)
- [A Unified Solution to Video Fusion: From Multi-Frame Learning to Benchmarking](https://mlsanity.com/paper/2505.19858/) (2025)
- [UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection](https://mlsanity.com/paper/2105.08590/) (2021)
- [Test-Time Dynamic Image Fusion](https://mlsanity.com/paper/2411.02840/) (2024)
- [DAE-Fuse: An Adaptive Discriminative Autoencoder for Multi-Modality Image Fusion](https://mlsanity.com/paper/2409.10080/) (2024)
- [Spatial-Frequency Enhanced Mamba for Multi-Modal Image Fusion](https://mlsanity.com/paper/2511.06593/) (2025)
- [Coupled Feature Learning for Multimodal Medical Image Fusion](https://mlsanity.com/paper/2102.08641/) (2021)
- [Unsupervised Image Fusion Using Deep Image Priors](https://mlsanity.com/paper/2110.09490/) (2021)
- [A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosis](https://mlsanity.com/paper/2505.14715/) (2025)
- [Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection](https://mlsanity.com/paper/2101.07342/) (2021)
- [Personalizing image enhancement for critical visual tasks: improved legibility of papyri using color processing and visual illusions](https://mlsanity.com/paper/2104.01106/) (2021)
- [Variable Augmented Network for Invertible Modality Synthesis-Fusion](https://mlsanity.com/paper/2109.00670/) (2021)
- [Multispectral image fusion based on super pixel segmentation](https://mlsanity.com/paper/2112.11329/) (2021)
- [SMFusion: Semantic-Preserving Fusion of Multimodal Medical Images for Enhanced Clinical Diagnosis](https://mlsanity.com/paper/2505.12251/) (2025)
- [Single Domain Generalization for Alzheimer's Detection from 3D MRIs with Pseudo-Morphological Augmentations and Contrastive Learning](https://mlsanity.com/paper/2505.22465/) (2025)
- [Text-Guided Channel Perturbation and Pretrained Knowledge Integration for Unified Multi-Modality Image Fusion](https://mlsanity.com/paper/2511.12432/) (2025)
- [Semantic Image Fusion](https://mlsanity.com/paper/2110.06697/) (2021)
- [Multimodal Query-guided Object Localization](https://mlsanity.com/paper/2212.00749/) (2022)
- [Decomposing and Coupling Saliency Map for Lesion Segmentation in Ultrasound Images](https://mlsanity.com/paper/2308.00947/) (2023)
- [Three-Dimensional Medical Image Fusion with Deformable Cross-Attention](https://mlsanity.com/paper/2310.06291/) (2023)
- [GAN-HA: A generative adversarial network with a novel heterogeneous dual-discriminator network and a new attention-based fusion strategy for infrared and visible image fusion](https://mlsanity.com/paper/2404.15992/) (2024)

## Abstract (arXiv)

Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.