Dictionary learning is the key process of sparse representation which is one of the most widely used image representation theories in image fusion. The existing dictionary learning method does not use the group structure information and the sparse coefficients well. In this paper, we propose a new adaptive structured dictionary learning algorithm and a ℓ1-norm maximum fusion rule that innovatively utilizes grouped sparse coefficients to merge the images. In the dictionary learning algorithm, we do not need prior knowledge about any group structure of the dictionary. By using the characteristics of the dictionary in expressing the signal, our algorithm can automatically find the desired potential structure information that hidden in the dictionary. The fusion rule takes the physical meaning of the group structure dictionary, and makes activity-level judgement on the structure information when the images are being merged. Therefore, the fused image can retain more significant information. Comparisons have been made with several state-of-the-art dictionary learning methods and fusion rules. The experimental results demonstrate that, the dictionary learning algorithm and the fusion rule both outperform others in terms of several objective evaluation metrics.
Image registration is an important pre-processing operation to perform multi-modal joint analysis correctly. However, registration of images captured by different sensors is a very challenging problem due to the apparent differences of scenes. Traditional Coherent Point Drift method (CPD) is a global registration approach, which strongly relies on the extracted features. In the case of infrared and visible images, registration methods based on edges or points are inappropriate since those features might be significantly different. Fortunately, depth information is more robust feature for multi-modal image pairs. In this paper, we propose an algorithm based on Canny to extract edge of objects. And the regions of interest (ROI) is obtained by depth maps of image pairs in which common features usually successfully implemented by point set registration. Experimental results on real world data demonstrate the effectiveness of the proposed approach, which is superior to the traditional CPD algorithm for multi-modal image registration.