Image Fusion Using Quaternion Wavelet Transform and Multiple Features

Multi-scale-based image fusion is one of main fusion methods, in which multi-scale decomposition tool and feature extraction play very important roles. The quaternion wavelet transform (QWT) is one of the effective multi-scale decomposition tools. Therefore, this paper proposes a novel multimodal image fusion method using QWT and multiple features. First, we perform QWT on each source image to obtain low-frequency coefficients and high-frequency coefficients. Second, a weighted average fusion rule based on the phase and magnitude of low-frequency subband and spatial variance is proposed to fuse the low-frequency subbands. Next, a choose-max fusion rule based on the contrast and energy of coefficient is proposed to integrate the high-frequency subbands. Finally, the final fused image is constructed by inverse QWT. The proposed method is conducted on multi-focus images, medical images, infrared-visible images, and remote sensing images, respectively. Experimental results demonstrate the effectiveness of the proposed method.