Infrared and Visible Image Fusion via Variational Bayesian Approximation Method

The integration of infrared and visible images has become increasingly relevant in the field of remote sensing technologies and image processing applications. This technique has proven beneficial in scenarios such as thermal source monitoring and anomaly detection for industrial machines. The fusion of these two images allows for the creation of a compound image that contains important information from both sources, resulting in an enhanced image with rich background details and clear targets. This paper presents a model for image fusion and probabilistic generation of infrared and visible images. The variational Bayesian approximation method is utilized to fuse the information from both images. The proposed method also introduces a hierarchical prior model using wavelet transform and a variational Bayesian inference algorithm to achieve information fusion. The proposed method provides a feasible and effective solution for health monitoring applications of high-speed direct-drive blowers in industrial scenarios.