Image Fusion Based on Discrete Chebyshev Moments

Though deep learning-based methods have demonstrated strong capabilities on image fusion, they usually improve the fusion performance by increasing the width and depth of the network, increasing the compu-tational effort and being unsuitable for industrial applications. In this paper, an end-to-end network based on fixed convolution module of discrete Chebyshev moments is proposed, which does not need any pre-or post-processing. The proposed network is roughly composed of three parts: feature extraction module, fusion module and feature reconstruction module. In the feature extraction module, a novel fixed convolution module based on discrete Chebyshev moments is proposed to obtain different frequency components in a short time. To improve the image sharpness and fuse more details, a spatial attention mechanism based on average gradient is proposed in fusion module. Extensive results demonstrate that the proposed network can achieve remarkable fusion performance, high time efficiency and strong generalization ability.