基于SGWT和多显著性的红外与可见光图像融合

由于谱图小波变换（Spectral Graph Wavelet Transform, SGWT）可充分利用图像在图域中的光谱特性，本文结合其对不规则小区域表达的优势，提出了一种基于多显著性的红外与可见光融合算法。首先应用SGWT将源图像分解成一个低频子带和若干个高频子带；对于低频系数，将多个互补的低层特征结合起来，提出了一种适合人眼视觉特征的多显著性融合规则，对于高频系数，充分考虑邻域像素的相关性，提出了一种区域绝对值取大规则；最后，应用了一种加权最小二乘优化（weighted least squares, WLS）方法对谱图小波重构的融合图像进行优化，在突出显著目标的同时尽可能多地保留可见光的背景细节。实验结果表明，与DWT（Discrete Wavelet Transform）、NSCT（Non-down Sampled Contourlet Transform）等7种相关算法相比，在突出红外目标的同时还能保留更多的可见光背景细节，具有较好的视觉效果；同时在方差、熵、Q_abf和互信息量4个客观评价上也均占据优势。

Infrared and Visible Image Fusion Based on SGWT and Multi-Saliency

Spectral graph wavelet transform (SGWT) can fully utilize the spectral characteristics of an image in the image domain and has advantages in the expression of small irregular regions. Therefore, this paper proposes an infrared and visible fusion algorithm based on multi saliency. First, SGWT is used to decompose the source image into a low-frequency sub-band and several high-frequency sub-bands. For low-frequency coefficients, a multi saliency fusion rule suitable for human visual features is proposed by combining multiple complementary low-level features. For high-frequency coefficients, a rule for increasing the absolute value of the region is proposed by fully considering the correlation of neighborhood pixels. Finally, a weighted least squares optimization method is applied to optimize the fusion image reconstructed by spectral wavelet reconstruction, which highlights the main target and retains the background details of visible light as much as possible. The experimental results show that, compared with seven related algorithms such as DWT and NSCT, this method can highlight the infrared target and retain more visible background details, resulting in a better visual effect. Moreover, it exhibits advantages in four objective evaluations: variance, entropy, Q_abf, and mutual information.