# Coupled Feature Learning for Multimodal Medical Image Fusion

arXiv: https://arxiv.org/abs/2102.08641 · HTML: https://mlsanity.com/paper/2102.08641/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 34 references that are in the map.
- First posted on arXiv 17 Feb 2021.

## Builds on (most cited first, 2 of 2)

- [Medical Image Fusion: A survey of the state of the art](https://mlsanity.com/paper/1401.0166/) (2013)
- [A Fast Dictionary Learning Method for Coupled Feature Space Learning](https://mlsanity.com/paper/1904.06968/) (2019)

## Built on by (most cited first, 4 of 4)

- [Efficient ADMM-based Algorithms for Convolutional Sparse Coding](https://mlsanity.com/paper/2109.02969/) (2021)
- [l0-Regularized Sparse Coding-based Interpretable Network for Multi-Modal Image Fusion](https://mlsanity.com/paper/2411.04519/) (2024)
- [Convolutional Simultaneous Sparse Approximation with Applications to RGB-NIR Image Fusion](https://mlsanity.com/paper/2203.09913/) (2022)
- [Combined Dictionary Unfolding Network with Gradient-Adaptive Fidelity for Transferable Multi-Source Fusion](https://mlsanity.com/paper/2605.00461/) (2026)

## Abstract (arXiv)

Multimodal image fusion aims to combine relevant information from images acquired with different sensors. In medical imaging, fused images play an essential role in both standard and automated diagnosis. In this paper, we propose a novel multimodal image fusion method based on coupled dictionary learning. The proposed method is general and can be employed for different medical imaging modalities. Unlike many current medical fusion methods, the proposed approach does not suffer from intensity attenuation nor loss of critical information. Specifically, the images to be fused are decomposed into coupled and independent components estimated using sparse representations with identical supports and a Pearson correlation constraint, respectively. An alternating minimization algorithm is designed to solve the resulting optimization problem. The final fusion step uses the max-absolute-value rule. Experiments are conducted using various pairs of multimodal inputs, including real MR-CT and MR-PET images. The resulting performance and execution times show the competitiveness of the proposed method in comparison with state-of-the-art medical image fusion methods.