Information Fusion

Aims & Scope

The journal is intended to present within a single forum all of the developments in the field of multi-sensor, multi-source, multi-process information fusion and thereby promote the synergism among the many disciplines that are contributing to its growth.

The journal is the premier vehicle for disseminating information on all aspects of research and development in the field of information fusion.

Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications.

Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	15.5
2024	14.70

Journal Rank

Year	Value
2024	393

Journal Citation Indicator

Year	Value
2024	15732

SJR (SCImago Journal Rank)

Year	Value
2024	4.128

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	179

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

  Citation: 6219

  Authors: Alejandro, Natalia, Javier, Adrien, Siham, Alberto, Salvador, Sergio, Daniel, Richard, Raja, Francisco

• A review of uncertainty quantification in deep learning: Techniques, applications and challenges

  Citation: 1776

  Authors: Moloud, Farhad, Sadiq, Dana, Li, Mohammad, Paul, Xiaochun, Abbas, U. Rajendra, Vladimir, Saeid

• FusionGAN: A generative adversarial network for infrared and visible image fusion

  Citation: 1695

  Authors: Jiayi, Wei, Pengwei, Chang, Junjun

• Multisensor data fusion: A review of the state-of-the-art

  Citation: 1507

  Authors: Bahador, Alaa, Fakhreddine O., Saiedeh N.

• Infrared and visible image fusion methods and applications: A survey

  Citation: 1320

  Authors: Jiayi, Yong, Chang

• A general framework for image fusion based on multi-scale transform and sparse representation

  Citation: 1240

  Authors: Yu, Shuping, Zengfu

• Tabular data: Deep learning is not all you need

  Citation: 1183

  Authors: Ravid, Amitai

• IFCNN: A general image fusion framework based on convolutional neural network

  Citation: 1147

  Authors: Yu, Yu, Peng, Han, Xiaolin, Li

• A review of affective computing: From unimodal analysis to multimodal fusion

  Citation: 1131

  Authors: Soujanya, Erik, Rajiv, Amir

• Multi-focus image fusion with a deep convolutional neural network

  Citation: 1127

  Authors: Yu, Xun, Hu, Zengfu

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