# A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Images

arXiv: https://arxiv.org/abs/2409.15332 · HTML: https://mlsanity.com/paper/2409.15332/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 7 Sep 2024.

## Builds on (most cited first, 15 of 15)

- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Space](https://mlsanity.com/paper/2309.14616/) (2023)
- [Survival Prediction Across Diverse Cancer Types Using Neural Networks](https://mlsanity.com/paper/2404.08713/) (2024)
- [Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example](https://mlsanity.com/paper/2404.08279/) (2024)
- [Exploring Diverse Methods in Visual Question Answering](https://mlsanity.com/paper/2404.13565/) (2024)
- [Contextual Hourglass Network for Semantic Segmentation of High Resolution Aerial Imagery](https://mlsanity.com/paper/1810.12813/) (2018)
- [Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks](https://mlsanity.com/paper/2405.11704/) (2024)
- [Application of Multimodal Fusion Deep Learning Model in Disease Recognition](https://mlsanity.com/paper/2406.18546/) (2024)
- [A comparative study of generative adversarial networks for image recognition algorithms based on deep learning and traditional methods](https://mlsanity.com/paper/2408.03568/) (2024)
- [Infrared Image Super-Resolution via Lightweight Information Split Network](https://mlsanity.com/paper/2405.10561/) (2024)
- [Enhancing Medical Imaging with GANs Synthesizing Realistic Images from Limited Data](https://mlsanity.com/paper/2406.18547/) (2024)
- [An Enhanced Encoder-Decoder Network Architecture for Reducing Information Loss in Image Semantic Segmentation](https://mlsanity.com/paper/2406.01605/) (2024)
- [Exploration of Multi-Scale Image Fusion Systems in Intelligent Medical Image Analysis](https://mlsanity.com/paper/2406.18548/) (2024)
- [Research on Deep Learning Model of Feature Extraction Based on Convolutional Neural Network](https://mlsanity.com/paper/2406.08837/) (2024)
- [Advancements in Feature Extraction Recognition of Medical Imaging Systems Through Deep Learning Technique](https://mlsanity.com/paper/2406.18549/) (2024)

## Built on by (most cited first, 11 of 11)

- [Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks](https://mlsanity.com/paper/2410.17617/) (2024)
- [Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout](https://mlsanity.com/paper/2411.06128/) (2024)
- [Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning](https://mlsanity.com/paper/2411.06326/) (2024)
- [Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification](https://mlsanity.com/paper/2411.12151/) (2024)
- [Adversarial Neural Networks in Medical Imaging Advancements and Challenges in Semantic Segmentation](https://mlsanity.com/paper/2410.13099/) (2024)
- [Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphs](https://mlsanity.com/paper/2411.15195/) (2024)
- [Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches](https://mlsanity.com/paper/2411.16567/) (2024)
- [Balancing Innovation and Privacy: Data Security Strategies in Natural Language Processing Applications](https://mlsanity.com/paper/2410.08553/) (2024)
- [Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study](https://mlsanity.com/paper/2410.20792/) (2024)
- [Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data](https://mlsanity.com/paper/2411.18622/) (2024)
- [Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Management](https://mlsanity.com/paper/2410.19211/) (2024)

## Abstract (arXiv)

This paper presents a lightweight image fusion algorithm specifically designed for merging visible light and infrared images, with an emphasis on balancing performance and efficiency. The proposed method enhances the generator in a Generative Adversarial Network (GAN) by integrating the Convolutional Block Attention Module (CBAM) to improve feature focus and utilizing Depthwise Separable Convolution (DSConv) for more efficient computations. These innovations significantly reduce the model's computational cost, including the number of parameters and inference latency, while maintaining or even enhancing the quality of the fused images. Comparative experiments using the M3FD dataset demonstrate that the proposed algorithm not only outperforms similar image fusion methods in terms of fusion quality but also offers a more resource-efficient solution suitable for deployment on embedded devices. The effectiveness of the lightweight design is validated through extensive ablation studies, confirming its potential for real-time applications in complex environments.