Infrared and visible images fusion by using sparse representation and guided filter

Infrared and visible images play an important role in transportation systems since they can monitor traffic conditions around the clock. However, visible images are susceptible to the imaging environments, and infrared images are not rich enough in detail. The infrared and visible images fusion techniques can fuze these two different modal images into a single image with more useful information. In this paper, we propose an effective infrared and visible images fusion method for traffic systems. The weight maps are measured by utilizing the sparse coefficients. The next is to decompose the infrared and visible pair into high-frequency layers (HFLs) and low-frequency layers (LFLs). Since the two layers contain different structures and texture information, to extract the representative component, the guided filter is utilized to optimize weight maps in accordance with the different characteristic of the infrared and visible pairs. The final step is to reconstruct the two-scale layers according to the weight maps. Experimental results demonstrate our method outperforms other popular approaches in terms of subjective perception and objective metrics.