# FCDFusion: a Fast, Low Color Deviation Method for Fusing Visible and Infrared Image Pairs

arXiv: https://arxiv.org/abs/2408.01080 · HTML: https://mlsanity.com/paper/2408.01080/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 57 references that are in the map.
- First posted on arXiv 2 Aug 2024.

## Builds on (most cited first, 3 of 3)

- [VIFB: A Visible and Infrared Image Fusion Benchmark](https://mlsanity.com/paper/2002.03322/) (2020)
- [Infrared and Visible Image Fusion with ResNet and zero-phase component analysis](https://mlsanity.com/paper/1806.07119/) (2018)
- [Infrared and visible image fusion using Latent Low-Rank Representation](https://mlsanity.com/paper/1804.08992/) (2018)

## Built on by (most cited first, 14 of 14)

- [Noise Calibration and Spatial-Frequency Interactive Network for STEM Image Enhancement](https://mlsanity.com/paper/2504.02555/) (2025)
- [RobustGS: Unified Boosting of Feedforward 3D Gaussian Splatting under Low-Quality Conditions](https://mlsanity.com/paper/2508.03077/) (2025)
- [Frequency-Dynamic Attention Modulation for Dense Prediction](https://mlsanity.com/paper/2507.12006/) (2025)
- [Refining Context-Entangled Content Segmentation via Curriculum Selection and Anti-Curriculum Promotion](https://mlsanity.com/paper/2602.01183/) (2026)
- [Enhancing Unregistered Hyperspectral Image Super-Resolution via Unmixing-based Abundance Fusion Learning](https://mlsanity.com/paper/2603.07918/) (2026)
- [Statistical Characteristic-Guided Denoising for Rapid High-Resolution Transmission Electron Microscopy Imaging](https://mlsanity.com/paper/2603.18834/) (2026)
- [Boosting Zero-shot Stereo Matching using Large-scale Mixed Images Sources in the Real World](https://mlsanity.com/paper/2505.08607/) (2025)
- [MAC-Lookup: Multi-Axis Conditional Lookup Model for Underwater Image Enhancement](https://mlsanity.com/paper/2507.02270/) (2025)
- [FusionCounting: Robust visible-infrared image fusion guided by crowd counting via multi-task learning](https://mlsanity.com/paper/2508.20817/) (2025)
- [SSCM: A Spatial-Semantic Consistent Model for Multi-Contrast MRI Super-Resolution](https://mlsanity.com/paper/2509.18593/) (2025)
- [ERIENet: An Efficient RAW Image Enhancement Network under Low-Light Environment](https://mlsanity.com/paper/2512.15186/) (2025)
- [Boosting Text-Driven Video Segmentation via Geometry-Aware Distillation](https://mlsanity.com/paper/2606.24464/) (2026)
- [Binarized High-Efficiency RAW Video Restoration and Beyond](https://mlsanity.com/paper/2608.16756/) (2026)
- [RDANet: Relative Degradation Aware Network for Infrared Small Target Detection](https://mlsanity.com/paper/2608.20870/) (2026)

## Abstract (arXiv)

Visible and infrared image fusion (VIF) aims to combine information from visible and infrared images into a single fused image. Previous VIF methods usually employ a color space transformation to keep the hue and saturation from the original visible image. However, for fast VIF methods, this operation accounts for the majority of the calculation and is the bottleneck preventing faster processing. In this paper, we propose a fast fusion method, FCDFusion, with little color deviation. It preserves color information without color space transformations, by directly operating in RGB color space. It incorporates gamma correction at little extra cost, allowing color and contrast to be rapidly improved. We regard the fusion process as a scaling operation on 3D color vectors, greatly simplifying the calculations. A theoretical analysis and experiments show that our method can achieve satisfactory results in only 7 FLOPs per pixel. Compared to state-of-the-art fast, color-preserving methods using HSV color space, our method provides higher contrast at only half of the computational cost. We further propose a new metric, color deviation, to measure the ability of a VIF method to preserve color. It is specifically designed for VIF tasks with color visible-light images, and overcomes deficiencies of existing VIF metrics used for this purpose. Our code is available at https://github.com/HeasonLee/FCDFusion.