Search

Infrared and visible image fusion is an effective image processing technique to obtain more comprehensive information, which can help people better understand various scenarios. In this paper, a novel infrared and visible image fusion method is proposed which fully considers the attributes of objects in source images. Benefiting from the attribute and the edge-preserving filters, the prominent objects in the infrared source image are effectively extracted. Then, the weight-based Laplacian pyramid fusion strategy is adopted to get more natural fusion results. The experimental results on the public image fusion datasets and a new infrared–visible video fusion dataset show that the proposed method achieves state-of-the-art fusion performance in terms of both visual and objective evaluations. The proposed algorithm is also implemented in an infrared–visible dual sensor system, which demonstrated the practicability of our fusion method. • A novel attribute guided image fusion method is proposed. • A dual infrared and visible imaging system is constructed. • A high-quality infrared–color visible image and video dataset is established. [ABSTRACT FROM AUTHOR]

Multi-modal image fusion is widely used in various fields, especially in military, medical, industrial detection and other fields. Image fusion can integrate redundant and complementary information of two or more multi-modal images into one image, so that the fused image contains more useful information. In this paper, we construct a novel dataset for Multi-modal Image Fusion Applications (MOFA), including four modals: visible, near-infrared (NIR), long-wavelength infrared (LWIR) and polarization. The MOFA dataset contains 1062 images of 118 groups, in which 450 are indoor and 612 are outdoor. The dataset is applied to different image fusion applications, including general multi-modal image fusion, fusion based image super-resolution and image restoration. Multiple image fusion methods are compared and analyzed on this dataset with a qualitative assessment of subjective and objective metrics. Based on the experiments, the advantages and disadvantages of different methods are discussed. Moreover, the challenging problems of image fusion are concluded. • Build a multi-modal imaging device. • Construct a multi-modal dataset, including visible, NIR, LWIR and polarization. • Explore the effectiveness of the dataset for different image processing tasks. • Analyze the advantages and disadvantages of different fusion methods. [ABSTRACT FROM AUTHOR]