# Interactive Feature Embedding for Infrared and Visible Image Fusion

arXiv: https://arxiv.org/abs/2211.04877 · HTML: https://mlsanity.com/paper/2211.04877/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 52 references that are in the map.
- First posted on arXiv 9 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [DenseFuse: A Fusion Approach to Infrared and Visible Images](https://mlsanity.com/paper/1804.08361/) (2018)
- [NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction](https://mlsanity.com/paper/1801.08297/) (2018)
- [DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs](https://mlsanity.com/paper/1712.07384/) (2017)
- [Unsupervised Domain-Specific Deblurring via Disentangled Representations](https://mlsanity.com/paper/1903.01594/) (2019)
- [Infrared and Visible Image Fusion using a Deep Learning Framework](https://mlsanity.com/paper/1804.06992/) (2018)

## Built on by (most cited first, 6 of 6)

- [ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learning](https://mlsanity.com/paper/2312.07943/) (2023)
- [All-weather Multi-Modality Image Fusion: Unified Framework and 100k Benchmark](https://mlsanity.com/paper/2402.02090/) (2024)
- [Bridging the Gap between Multi-focus and Multi-modal: A Focused Integration Framework for Multi-modal Image Fusion](https://mlsanity.com/paper/2311.01886/) (2023)
- [UMCFuse: A Unified Multiple Complex Scenes Infrared and Visible Image Fusion Framework](https://mlsanity.com/paper/2402.02096/) (2024)
- [CFMW: Cross-modality Fusion Mamba for Robust Object Detection under Adverse Weather](https://mlsanity.com/paper/2404.16302/) (2024)
- [DIFF-MF: A Difference-Driven Channel-Spatial State Space Model for Multi-Modal Image Fusion](https://mlsanity.com/paper/2601.05538/) (2026)

## Abstract (arXiv)

General deep learning-based methods for infrared and visible image fusion rely on the unsupervised mechanism for vital information retention by utilizing elaborately designed loss functions. However, the unsupervised mechanism depends on a well designed loss function, which cannot guarantee that all vital information of source images is sufficiently extracted. In this work, we propose a novel interactive feature embedding in self-supervised learning framework for infrared and visible image fusion, attempting to overcome the issue of vital information degradation. With the help of self-supervised learning framework, hierarchical representations of source images can be efficiently extracted. In particular, interactive feature embedding models are tactfully designed to build a bridge between the self-supervised learning and infrared and visible image fusion learning, achieving vital information retention. Qualitative and quantitative evaluations exhibit that the proposed method performs favorably against state-of-the-art methods.