MMFuse: A multi-scale infrared and visible images fusion algorithm based on morphological reconstruction and membership filtering

This study proposes a multi-scale transformation method based on morphological reconstruction and membership filtering, termed as MMFuse, to fuse infrared and visible images. This method employs a fuzzy c-means clustering algorithm for multi-scale decomposition by introducing morphological reconstruction operations and modifying member partitions to ensure noise resistance and image detail preservation. In addition, the MMFuse utilises the image attributes of layers as their fusion weights at each scale for adaptive feature fusion, which reduces the difficulty of manual adjustment of fusion weights. Moreover, on the basis of histogram enhancement, a visible image enhancement method is proposed, which can help exploit additional texture details in low-light visible images and transfer these details to the fused image. The experiments performed on public datasets indicates that the MMFuse can generate sharp and clean fused images with high robustness and good fusion results for the images corrupted by different noises. Moreover, the results of this method appear as high-quality visible images with clear highlighted infrared targets.