结构和纹理感知的Retinex融合红外与可见光图像

Fusion of infrared and visible images via structure and texture-aware retinex

为了提高红外与可见光图像的融合质量,本文提出一种新颖的基于结构和纹理感知的Retinex模型的红外与可见光图像融合方法。该方法首先通过结构和纹理感知的Retinex模型将源图像分解为反射分量和光照分量,不但能够有效地将源图像的纹理和结构信息进行分离,而且也能很好地提取可见光图像中低亮度下的细节特征信息;然后通过构造源图像的二阶梯度为基础的权值映射和伽马函数对反射分量和光照分量进行融合;最后对融合的反射分量和光照分量进行重建得到最终融合图像。通过对38组广泛使用的TNO红外/可见光图像数据库中的图像进行测试表明,本文方法得到的融合图像不但具有较高的可视化质量,而且与近年来提出的5种高水平方法相比,本文方法在互信息、非线性相关信息熵、图像相位一致性度量上取得了更好的客观评价结果,能够较好地解决红外与可见光图像融合中出现的细节特征缺失和对比度较低的问题。

To improve the quality of the fusion of infrared and visible images,this study proposes a novel method based on structure and texture-aware Retinex(STAR). It first decomposes the source images into reflection and illumination components according to the STAR model. This decomposition can separate the texture and structure of the source images accurately and extract the detailed features of the visible im-ages with low luminance. Subsequently,it merges the reflection component using a weight map,which is constructed using the second-order gradient of the source images as the input. Moreover,it merges the illumination component using a gamma function,which can make the fused image have more brightness information. Finally,it reconstructs the fused reflection and illumination components to obtain the final fusion image. According to the test on 38 pairs of widely used images in the TNO infrared and visible image database,the proposed method can generate excellent fused results with high visual quality. Furthermore,compared with five state-of-the-art methods for the fusion of infrared and visible images,the proposed method achieved significantly better objective evaluation results in mutual information,nonlinear correlation information entropy,and feature measurement based on image phase consistency. This study involves the use of STAR model for fusing infrared and visible images and establishes a direct fusion framework based on Retinex,which improves the fusion results of the existing methods in terms of detailed features and global contrast.