Infrared-visible Image Fusion Using Accelerated Convergent Convolutional Dictionary Learning

Techniques for the fusion of infrared and visible images have gradually become a popular research topic in the field of computer vision. In our paper, accelerated convergent convolutional dictionary learning (CDL) is first introduced for infrared-visible image fusion. The proposed method combines the advantages of CDL and convolutional sparse representation (CSR) while also compensating for model mismatches between the training and fusion stages. Each image is decomposed into a base layer and a detail layer, for which different fusion strategies are used. Unlike previous CSR/CDL-based fusion methods, we introduce a practical and convergent Fast Block Proximal Gradient Using a Diagonal Majorizer (FBPG-M) method with two-block and multiblock schemes into the detail layer. Influenced by various imaging mechanisms, an ‘averaging’ fusion strategy is used for the base layer. Our method is evaluated and compared qualitatively and quantitatively with five typical fusion methods on 10 public datasets. The model is both subjectively and objectively evaluated, and the results show that the proposed method achieves notable success in terms of preserving details and focusing on targets.