# NestFuse: An Infrared and Visible Image Fusion Architecture based on Nest Connection and Spatial/Channel Attention Models

arXiv: https://arxiv.org/abs/2007.00328 · HTML: https://mlsanity.com/paper/2007.00328/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2020.

## Built on by (most cited first, 30 of 62)

- [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)
- [RFN-Nest: An end-to-end residual fusion network for infrared and visible images](https://mlsanity.com/paper/2103.04286/) (2021)
- [FusionMamba: Dynamic Feature Enhancement for Multimodal Image Fusion with Mamba](https://mlsanity.com/paper/2404.09498/) (2024)
- [Dif-Fusion: Towards High Color Fidelity in Infrared and Visible Image Fusion with Diffusion Models](https://mlsanity.com/paper/2301.08072/) (2023)
- [Multi-modal Gated Mixture of Local-to-Global Experts for Dynamic Image Fusion](https://mlsanity.com/paper/2302.01392/) (2023)
- [A Task-guided, Implicitly-searched and Meta-initialized Deep Model for Image Fusion](https://mlsanity.com/paper/2305.15862/) (2023)
- [MambaDFuse: A Mamba-based Dual-phase Model for Multi-modality Image Fusion](https://mlsanity.com/paper/2404.08406/) (2024)
- [TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network](https://mlsanity.com/paper/2201.10147/) (2022)
- [TextFusion: Unveiling the Power of Textual Semantics for Controllable Image Fusion](https://mlsanity.com/paper/2312.14209/) (2023)
- [SwinFuse: A Residual Swin Transformer Fusion Network for Infrared and Visible Images](https://mlsanity.com/paper/2204.11436/) (2022)
- [Image Fusion Transformer](https://mlsanity.com/paper/2107.09011/) (2021)
- [E2E-MFD: Towards End-to-End Synchronous Multimodal Fusion Detection](https://mlsanity.com/paper/2403.09323/) (2024)
- [MaeFuse: Transferring Omni Features with Pretrained Masked Autoencoders for Infrared and Visible Image Fusion via Guided Training](https://mlsanity.com/paper/2404.11016/) (2024)
- [FusionBooster: A Unified Image Fusion Boosting Paradigm](https://mlsanity.com/paper/2305.05970/) (2023)
- [Infrared and Visible Image Fusion with Language-Driven Loss in CLIP Embedding Space](https://mlsanity.com/paper/2402.16267/) (2024)
- [S4Fusion: Saliency-aware Selective State Space Model for Infrared Visible Image Fusion](https://mlsanity.com/paper/2405.20881/) (2024)
- [PPT Fusion: Pyramid Patch Transformerfor a Case Study in Image Fusion](https://mlsanity.com/paper/2107.13967/) (2021)
- [Deep Unfolding Multi-modal Image Fusion Network via Attribution Analysis](https://mlsanity.com/paper/2502.01467/) (2025)
- [Temporal Aggregation for Adaptive RGBT Tracking](https://mlsanity.com/paper/2201.08949/) (2022)
- [A Survey for Deep RGBT Tracking](https://mlsanity.com/paper/2201.09296/) (2022)
- [Visual Object Tracking on Multi-modal RGB-D Videos: A Review](https://mlsanity.com/paper/2201.09207/) (2022)
- [A Unified Multi-Task Learning Framework of Real-Time Drone Supervision for Crowd Counting](https://mlsanity.com/paper/2202.03843/) (2022)
- [A Multi-scale Information Integration Framework for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2312.04328/) (2023)
- [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)
- [UFA-FUSE: A novel deep supervised and hybrid model for multi-focus image fusion](https://mlsanity.com/paper/2101.04506/) (2021)
- [Video Is Graph: Structured Graph Module for Video Action Recognition](https://mlsanity.com/paper/2110.05904/) (2021)
- [Infrared and Visible Image Fusion via Interactive Compensatory Attention Adversarial Learning](https://mlsanity.com/paper/2203.15337/) (2022)
- [CHITNet: A Complementary to Harmonious Information Transfer Network for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2309.06118/) (2023)

## Abstract (arXiv)

In this paper we propose a novel method for infrared and visible image fusion where we develop nest connection-based network and spatial/channel attention models. The nest connection-based network can preserve significant amounts of information from input data in a multi-scale perspective. The approach comprises three key elements: encoder, fusion strategy and decoder respectively. In our proposed fusion strategy, spatial attention models and channel attention models are developed that describe the importance of each spatial position and of each channel with deep features. Firstly, the source images are fed into the encoder to extract multi-scale deep features. The novel fusion strategy is then developed to fuse these features for each scale. Finally, the fused image is reconstructed by the nest connection-based decoder. Experiments are performed on publicly available datasets. These exhibit that our proposed approach has better fusion performance than other state-of-the-art methods. This claim is justified through both subjective and objective evaluation. The code of our fusion method is available at https://github.com/hli1221/imagefusion-nestfuse