# Quaternion Infrared Visible Image Fusion

arXiv: https://arxiv.org/abs/2505.02364 · HTML: https://mlsanity.com/paper/2505.02364/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 5 May 2025.

## Builds on (most cited first, 12 of 12)

- [Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection](https://mlsanity.com/paper/2203.16220/) (2022)
- [LLVIP: A Visible-infrared Paired Dataset for Low-light Vision](https://mlsanity.com/paper/2108.10831/) (2021)
- [DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion](https://mlsanity.com/paper/2303.06840/) (2023)
- [LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images](https://mlsanity.com/paper/2304.05172/) (2023)
- [Dif-Fusion: Towards High Color Fidelity in Infrared and Visible Image Fusion with Diffusion Models](https://mlsanity.com/paper/2301.08072/) (2023)
- [MDLatLRR: A novel decomposition method for infrared and visible image fusion](https://mlsanity.com/paper/1811.02291/) (2018)
- [An Interactively Reinforced Paradigm for Joint Infrared-Visible Image Fusion and Saliency Object Detection](https://mlsanity.com/paper/2305.09999/) (2023)
- [Quaternion-based bilinear factor matrix norm minimization for color image inpainting](https://mlsanity.com/paper/2005.02886/) (2020)
- [Partial Sum Minimization of Singular Values in Robust PCA: Algorithm and Applications](https://mlsanity.com/paper/1503.01444/) (2015)
- [A general framework for constrained convex quaternion optimization](https://mlsanity.com/paper/2102.02763/) (2021)
- [Quaternion higher-order singular value decomposition and its applications in color image processing](https://mlsanity.com/paper/2101.00364/) (2021)
- [A New Cross-Space Total Variation Regularization Model for Color Image Restoration with Quaternion Blur Operator](https://mlsanity.com/paper/2405.12114/) (2024)

## Abstract (arXiv)

Visible images provide rich details and color information only under well-lighted conditions while infrared images effectively highlight thermal targets under challenging conditions such as low visibility and adverse weather. Infrared-visible image fusion aims to integrate complementary information from infrared and visible images to generate a high-quality fused image. Existing methods exhibit critical limitations such as neglecting color structure information in visible images and performance degradation when processing low-quality color-visible inputs. To address these issues, we propose a quaternion infrared-visible image fusion (QIVIF) framework to generate high-quality fused images completely in the quaternion domain. QIVIF proposes a quaternion low-visibility feature learning model to adaptively extract salient thermal targets and fine-grained texture details from input infrared and visible images respectively under diverse degraded conditions. QIVIF then develops a quaternion adaptive unsharp masking method to adaptively improve high-frequency feature enhancement with balanced illumination. QIVIF further proposes a quaternion hierarchical Bayesian fusion model to integrate infrared saliency and enhanced visible details to obtain high-quality fused images. Extensive experiments across diverse datasets demonstrate that our QIVIF surpasses state-of-the-art methods under challenging low-visibility conditions.