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

TL;DR

A fast fusion method, FCDFusion, with little color deviation, that preserves color information without color space transformations, by directly operating in RGB color space, and introduces a new metric, color deviation, to measure the ability of a VIF method to preserve color.

Abstract

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.

Figures (9)

• Figure 1: Color-preservation strategy of VIF methods using a color space transformation $T$.
• Figure 2: Color vectors in RGB color space.
• Figure 3: FCDFusion Framework. Gamma correction uses $\gamma=2$. Scaling multiplies each input RGB component by the same factor $k$.
• Figure 4: Color comparison of three fused images (a), (b), and (c), obtained by RGB-AVG, MST-SR, and our method, respectively. Only (c) presents a good visual effect and retains color information.
• Figure 5: Directional comparison of input color vectors (i.e., $\mathbf{c}_\text{v}$ and $\mathbf{c}_\text{i}$) and fused color vectors (i.e., $\mathbf{c}_\text{f-RGB}$, $\mathbf{c}_\text{f-YIQ}$, $\mathbf{c}_\text{f-HSV}$, and $\mathbf{c}_\text{f-Ours}$).