# Infrared and Visible Image Fusion using a Deep Learning Framework

arXiv: https://arxiv.org/abs/1804.06992 · HTML: https://mlsanity.com/paper/1804.06992/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2018.

## Built on by (most cited first, 30 of 33)

- [RFN-Nest: An end-to-end residual fusion network for infrared and visible images](https://mlsanity.com/paper/2103.04286/) (2021)
- [LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images](https://mlsanity.com/paper/2304.05172/) (2023)
- [CoCoNet: Coupled Contrastive Learning Network with Multi-level Feature Ensemble for Multi-modality Image Fusion](https://mlsanity.com/paper/2211.10960/) (2022)
- [Deep Learning for HDR Imaging: State-of-the-Art and Future Trends](https://mlsanity.com/paper/2110.10394/) (2021)
- [CrossFuse: A Novel Cross Attention Mechanism based Infrared and Visible Image Fusion Approach](https://mlsanity.com/paper/2406.10581/) (2024)
- [TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network](https://mlsanity.com/paper/2201.10147/) (2022)
- [Image Fusion Transformer](https://mlsanity.com/paper/2107.09011/) (2021)
- [From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2401.00421/) (2023)
- [Conditional Controllable Image Fusion](https://mlsanity.com/paper/2411.01573/) (2024)
- [Pixel-aligned RGB-NIR Stereo Imaging and Dataset for Robot Vision](https://mlsanity.com/paper/2411.18025/) (2024)
- [Interactive Feature Embedding for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2211.04877/) (2022)
- [FIRe-GAN: A novel Deep Learning-based infrared-visible fusion method for wildfire imagery](https://mlsanity.com/paper/2101.11745/) (2021)
- [Fusion of Single and Integral Multispectral Aerial Images](https://mlsanity.com/paper/2311.17515/) (2023)
- [A Multi-scale Information Integration Framework for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2312.04328/) (2023)
- [CHITNet: A Complementary to Harmonious Information Transfer Network for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2309.06118/) (2023)
- [Deep Learning for Spatio-Temporal Fusion in Land Surface Temperature Estimation: A Comprehensive Survey, Experimental Analysis, and Future Trends](https://mlsanity.com/paper/2412.16631/) (2024)
- [FusionVAE: A Deep Hierarchical Variational Autoencoder for RGB Image Fusion](https://mlsanity.com/paper/2209.11277/) (2022)
- [An FPGA smart camera implementation of segmentation models for drone wildfire imagery](https://mlsanity.com/paper/2309.01318/) (2023)
- [Unsupervised Image Fusion Using Deep Image Priors](https://mlsanity.com/paper/2110.09490/) (2021)
- [Infrared and visible image fusion based on Multi-State Contextual Hidden Markov Model](https://mlsanity.com/paper/2201.10739/) (2022)
- [D2-LRR: A Dual-Decomposed MDLatLRR Approach for Medical Image Fusion](https://mlsanity.com/paper/2206.15179/) (2022)
- [DePF: A Novel Fusion Approach based on Decomposition Pooling for Infrared and Visible Images](https://mlsanity.com/paper/2305.17376/) (2023)
- [Visible and Infrared Image Fusion Using Encoder-Decoder Network](https://mlsanity.com/paper/2412.08073/) (2024)
- [Maritime Small Object Detection from UAVs using Deep Learning with Altitude-Aware Dynamic Tiling](https://mlsanity.com/paper/2511.19728/) (2025)
- [Semantic Image Fusion](https://mlsanity.com/paper/2110.06697/) (2021)
- [Res2NetFuse: A Novel Res2Net-based Fusion Method for Infrared and Visible Images](https://mlsanity.com/paper/2112.14540/) (2021)
- [Unsupervised Image Fusion Method based on Feature Mutual Mapping](https://mlsanity.com/paper/2201.10152/) (2022)
- [Explicit and implicit models in infrared and visible image fusion](https://mlsanity.com/paper/2206.09581/) (2022)
- [Visible and infrared self-supervised fusion trained on a single example](https://mlsanity.com/paper/2307.04100/) (2023)
- [Fusion of Infrared and Visible Images based on Spatial-Channel Attentional Mechanism](https://mlsanity.com/paper/2308.13672/) (2023)

## Abstract (arXiv)

In recent years, deep learning has become a very active research tool which is used in many image processing fields. In this paper, we propose an effective image fusion method using a deep learning framework to generate a single image which contains all the features from infrared and visible images. First, the source images are decomposed into base parts and detail content. Then the base parts are fused by weighted-averaging. For the detail content, we use a deep learning network to extract multi-layer features. Using these features, we use l_1-norm and weighted-average strategy to generate several candidates of the fused detail content. Once we get these candidates, the max selection strategy is used to get final fused detail content. Finally, the fused image will be reconstructed by combining the fused base part and detail content. The experimental results demonstrate that our proposed method achieves state-of-the-art performance in both objective assessment and visual quality. The Code of our fusion method is available at https://github.com/hli1221/imagefusion_deeplearning