An Efficient Method for Infrared and Visual Images Fusion Based on Visual Attention Technique

Abstract

Infrared and visible image fusion technology provides many benefits for human vision and computer image processing tasks, including enriched useful information and enhanced surveillance capabilities. However, existing fusion algorithms have faced a great challenge to effectively integrate visual features from complex source images. In this paper, we design a novel infrared and visible image fusion algorithm based on visual attention technology, in which a special visual attention system and a feature fusion strategy based on the saliency maps are proposed. Special visual attention system first utilizes the co-occurrence matrix to calculate the image texture complication, which can select a particular modality to compute a saliency map. Moreover, we improved the iterative operator of the original visual attention model (VAM), a fair competition mechanism is designed to ensure that the visual feature in detail regions can be extracted accurately. For the feature fusion strategy, we use the obtained saliency map to combine the visual attention features, and appropriately enhance the tiny features to ensure that the weak targets can be observed. Different from the general fusion algorithm, the proposed algorithm not only preserve the interesting region but also contain rich tiny details, which can improve the visual ability of human and computer. Moreover, experimental results in complicated ambient conditions show that the proposed algorithm in this paper outperforms state-of-the-art algorithms in both qualitative and quantitative evaluations, and this study can extend to the field of other-type image fusion.