Integrating visible and infrared images into one high-quality image, also
known as visible and infrared image fusion, is a challenging yet critical task
for many downstream vision tasks. Most existing works utilize pretrained deep
neural networks or design sophisticated frameworks with strong priors for this
task, which may be unsuitable or lack flexibility. This paper presents
SimpleFusion, a simple yet effective framework for visible and infrared image
fusion. Our framework follows the decompose-and-fusion paradigm, where the
visible and the infrared images are decomposed into reflectance and
illumination components via Retinex theory and followed by the fusion of these
corresponding elements. The whole framework is designed with two plain
convolutional neural networks without downsampling, which can perform image
decomposition and fusion efficiently. Moreover, we introduce decomposition loss
and a detail-to-semantic loss to preserve the complementary information between
the two modalities for fusion. We conduct extensive experiments on the
challenging benchmarks, verifying the superiority of our method over previous
state-of-the-arts. Code is available at
\href{https://github.com/hxwxss/SimpleFusion-A-Simple-Fusion-Framework-for-Infrared-and-Visible-Images}{this https URL}

将可见光和红外图像融合成一幅高质量图像是许多下游视觉任务中具有挑战性但关键的任务。本文提出了一种简单而有效的可见光和红外图像融合框架 SimpleFusion。该框架通过 Retinex 理论将可见光和红外图像分解成反射和光照部分，并进行相应元素的融合。整个框架采用两个简单的卷积神经网络设计，无需降采样，能够高效地执行图像分解和融合。此外，我们引入了分解损失和细节到语义损失来保留两种模态之间的互补信息进行融合。通过在具有挑战性的基准测试上进行广泛实验，验证了我们方法在之前最先进方法上的优越性。

SimpleFusion：红外和可见光图像的简单融合框架

SimpleFusion: A Simple Fusion Framework for Infrared and Visible Images

Visible and infrared image fusion is one of the most important areas in image
processing due to its numerous applications. While much progress has been made
in recent years with efforts on developing fusion algorithms, there is a lack
of code library and benchmark which can gauge the state-of-the-art. In this
paper, after briefly reviewing recent advances of visible and infrared image
fusion, we present a visible and infrared image fusion benchmark (VIFB) which
consists of 21 image pairs, a code library of 20 fusion algorithms and 13
evaluation metrics. We also carry out large scale experiments within the
benchmark to understand the performance of these algorithms. By analyzing
qualitative and quantitative results, we identify effective algorithms for
robust image fusion and give some observations on the status and future
prospects of this field.

本文提出了一个可用于衡量可见光和红外图像融合最新进展的代码库和基准，包含 21 对图像、20 个融合算法和 13 个评价指标，通过大规模实验确定了有效的算法，同时对该领域的现状和未来前景进行了分析。