Tan Wei, Zhou Huixin, Song Jiangluqi, Li Huan, Yu Yue, Du Juan
Appl Opt. 2019 Apr 20;58(12):3064-3073. doi: 10.1364/AO.58.003064.
The aim of infrared and visible image fusion is to obtain an integrated image that contains obvious object information and high spatial resolution background information. The integrated image is more conductive for a human or a machine to understand and mine the information contained in the image. To attain this purpose, a fusion algorithm based on multi-level Gaussian curvature filtering (MLGCF) image decomposition is proposed. First, a MLGCF is presented and employed to decompose the input source images into three different layers: small-scale, large-scale, and base layers. Then, three fusion strategies-max-value, integrated, and energy-based-are applied to combine the three types of layers, which are based on the different properties of the three types of layers. Finally, the fusion image is reconstructed by summing the three types of fused layers. Six groups of experiments demonstrate that the proposed algorithm performs effectively in most cases by subjective and objective evaluations and even exceeds many high-level fusion algorithms.
红外与可见光图像融合的目的是获得一幅包含明显目标信息和高空间分辨率背景信息的综合图像。该综合图像更有利于人类或机器理解和挖掘图像中包含的信息。为实现这一目的，提出了一种基于多级高斯曲率滤波（MLGCF）图像分解的融合算法。首先，提出并采用MLGCF将输入的源图像分解为三个不同的层：小尺度层、大尺度层和基底层。然后，基于这三种类型层的不同特性，应用三种融合策略——最大值法、综合法和基于能量法——来组合这三种类型的层。最后，通过对三种融合层求和来重建融合图像。六组实验表明，所提出的算法在大多数情况下通过主观和客观评估都能有效执行，甚至超过了许多高级融合算法。
