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

TL;DR

FCDFusion is a rapid visible-infrared image fusion method that preserves color with minimal computation by operating directly in RGB space and incorporating gamma correction, outperforming existing fast methods in contrast and efficiency.

Contribution

The paper introduces FCDFusion, a novel fast fusion technique that maintains color fidelity without complex color space transformations, reducing computational cost significantly.

Findings

Achieves satisfactory fusion results with only 7 FLOPs per pixel.

Provides higher contrast than existing fast, color-preserving methods.

Offers a new metric, color deviation, for evaluating color preservation in VIF.

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.