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.

Infrared and Visible Image Fusion Based on Iterative Control of Anisotropic Diffusion and Regional Gradient Structure

“…In recent years, UAVs have played an increasingly important role in many fields due to their high flexibility, low cost, and easy operation, which are often used for battlefield reconnaissance, battle situation assessment, target recognition, and tracking in the military. Now, image sensors in UAVS can acquire multiple types of images such as multispectral images, visible images, and infrared images [1]. However, due to the limitation of environmental conditions such as light, imaging with only one sensor will be affected by certain factors and cannot meet the requirements of practical applications.…”

MIFFuse: A Multi-Level Feature Fusion Network for Infrared and Visible Images

“…The task of image fusion is to fuse multi-source information from multiple images into one image, which is convenient for people to view and postprocess [1]. The uses of image fusion are mainly divided into four categories, including medical image fusion [2], [3],multi-focus image fusion [4]- [6], remote sensing image fusion [7], [8], infrared and visual image fusion [9], [10], which are examples of image fusion. The far more common image fusion scenario is infrared and visible image fusion [11].…”

A Novel Medical Image Fusion Method Using Multi-Channel Pulse Coupled Neural Networks

“…With the gradual deepening of research on single-and dual-channel PCNN, the method is becoming one of the most popular method in the image fusion. Compared with the wavelet transform [1] that has been applied to multi-focus image fusion, FSD [2] and Gradient pyramid [3], PCNN is still a research focus of multi-focus image fusion, medical image fusion, and the well-known works are [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”

“…In literature [18] ,a novel fusion framework for infrared and visual images based on a full convolutional network (FCN) in the local non-subsampled shearlet transform (LNSST) domain is proposed. In other areas of research, such as literature [19] ,Visual Attention Technique(VAT) is mentioned. In PCNN models research, because both the two kind of image fusion methods above need to input the model for operations, which will bring the problem of low efficiency.…”