# SMLNet: A SPD Manifold Learning Network for Infrared and Visible Image Fusion

arXiv: https://arxiv.org/abs/2411.10679 · HTML: https://mlsanity.com/paper/2411.10679/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 54 references that are in the map.
- First posted on arXiv 16 Nov 2024.

## Builds on (most cited first, 22 of 22)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion](https://mlsanity.com/paper/2211.14461/) (2022)
- [DenseFuse: A Fusion Approach to Infrared and Visible Images](https://mlsanity.com/paper/1804.08361/) (2018)
- [A Riemannian Network for SPD Matrix Learning](https://mlsanity.com/paper/1608.04233/) (2016)
- [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)
- [Riemannian batch normalization for SPD neural networks](https://mlsanity.com/paper/1909.02414/) (2019)
- [MDLatLRR: A novel decomposition method for infrared and visible image fusion](https://mlsanity.com/paper/1811.02291/) (2018)
- [CrossFuse: A Novel Cross Attention Mechanism based Infrared and Visible Image Fusion Approach](https://mlsanity.com/paper/2406.10581/) (2024)
- [An Interactively Reinforced Paradigm for Joint Infrared-Visible Image Fusion and Saliency Object Detection](https://mlsanity.com/paper/2305.09999/) (2023)
- [TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework using Self-Supervised Multi-Task Learning](https://mlsanity.com/paper/2112.01030/) (2021)
- [GeomNet: A Neural Network Based on Riemannian Geometries of SPD Matrix Space and Cholesky Space for 3D Skeleton-Based Interaction Recognition](https://mlsanity.com/paper/2111.13089/) (2021)
- [TextFusion: Unveiling the Power of Textual Semantics for Controllable Image Fusion](https://mlsanity.com/paper/2312.14209/) (2023)
- [FusionBooster: A Unified Image Fusion Boosting Paradigm](https://mlsanity.com/paper/2305.05970/) (2023)
- [MMDRFuse: Distilled Mini-Model with Dynamic Refresh for Multi-Modality Image Fusion](https://mlsanity.com/paper/2408.15641/) (2024)

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

- [GrFormer: A Novel Transformer on Grassmann Manifold for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2506.14384/) (2025)
- [Rethinking Few-Shot Image Fusion: Granular Ball Priors Enable General-Purpose Deep Fusion](https://mlsanity.com/paper/2504.08937/) (2025)
- [Riemannian Networks over Full-Rank Correlation Matrices](https://mlsanity.com/paper/2605.19073/) (2026)
- [Text-Driven Fusion for Infrared and Visible Images: Achieving Image Scene Adaptation on Hyperbolic Space](https://mlsanity.com/paper/2606.15104/) (2026)

## Abstract (arXiv)

Euclidean representation learning methods have achieved promising results in image fusion tasks, which can be attributed to their clear advantages in handling with linear space. However, data collected from a realistic scene usually has a non-Euclidean structure, evaluating the consistency of latent representations from paired views using Euclidean distance raises challenges. To address this issue, a novel SPD (symmetric positive definite) manifold learning is proposed for multi-modal image fusion, named SMLNet, which extends the image fusion approach from the Euclidean space to the SPD manifolds. Specifically, we encode images according to the Riemannian geometry to exploit their intrinsic statistical correlations, thereby aligning with human visual perception. The SPD matrix fundamentally underpins our network's learning process. Building upon this mathematical foundation, we employ a cross-modal fusion strategy to exploit modality-specific dependencies and augment complementary information. To capture semantic similarity in images' intrinsic space, we further develop an attention module that meticulously processes the cross-modal semantic affinity matrix. Based on this, we design an end-to-end fusion network based on cross-modal manifold learning. Extensive experiments on public datasets demonstrate that our framework exhibits superior performance compared to the current state-of-the-art methods. Our code will be publicly available at https://github.com/Shaoyun2023.