Advances in Data Fusion Architectures

Coraluppi, Stefano; Carthel, Craig

doi:10.1002/9781118450550.ch09

Cited by 1 publication

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, other GAN-based methods have been proposed by various researchers 30 – 32 Due to the current limitations of end-to-end methods, such as blurry generation, limited preservation of details, and suboptimal fusion performance without proper constraints or sufficient training data, this study aims to improve upon existing GAN-based fusion methods.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, there has been a rapid advancement in IVIF benchmark datasets. These datasets include the TNO image fusion, 32 INO videos analytics, OSU color-thermal, RoadScene, 33 and multispectral 34 datasets. The TNO dataset stands out as the most widely used publicly available dataset for IVIF, encompassing 261 pairs of multispectral images captured during both daytime and nighttime.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Infrared and visible image fusion method based on hierarchical attention mechanism

Li,

Yan,

Luo

2024

J. Electron. Imag.

View full text Add to dashboard Cite

Currently, most feature extraction methods lack generality and require specific feature extraction methods for images from different sensors, particularly in the detection of faults in power equipment. However, the challenge lies in how to restore realistic texture details while correcting the color distortion. To address this issue, we propose an innovative infrared-visible light image fusion technique that combines hierarchical attention modules and collaborative refinement modules to facilitate feature fusion by jointly preserving intricate details and correcting lighting conditions. The hierarchical attention module aims to provide two different attention weight maps, which help select the most salient information from the source images in the infrared and visible light domains. This ultimately produces intermediate fusion results with comprehensive and complementary data. The collaborative refinement module consists of an edge enhancement network and a lighting correction network, which works together to enhance edge details and correct color differences. Experimental results validate the effectiveness of this method in successfully fusing the two types of images. The results demonstrate that, compared with several mainstream fusion methods, this method exhibits significant advantages in publicly available datasets and scenarios, such as power equipment detection.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%