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.