Infrared and Visible Image Fusion Using Threshold Segmentation and Weight Optimization

Infrared and visible image fusion can combine the thermal radiation information of infrared images with the detail information of visible images to generate images that are more suitable for human perception and machine processing. In this article, a novel infrared and visible image fusion method is proposed, which can sufficiently preserve infrared target information by multithreshold segmentation based on maximum entropy. Meanwhile, a retinex-based brightness correction method is used for visible image enhancement, which can adaptively enhance the brightness and contrast of low-light images. Furthermore, to make the proposed method more robust, the weighting coefficients are determined by optimization using the modified structural similarity (SSIM) as the objective function. The proposed method is compared with seven state-of-the-art methods on publicly available datasets, and the experimental results demonstrate that our method achieves better performances in both quantitative evaluation and visual effects.