Journal of Image and Graphics

Aims & Scope

"Chinese Journal of Image and Graphics" is co-founded by the Aerospace Information Innovation Institute of the Chinese Academy of Sciences, the Chinese Image and Graphics Society, and the Beijing Institute of Applied Physics and Computational Mathematics.

A comprehensive academic journal integrating the industrialization of achievements. "Chinese Journal of Image and Graphics" is a core Chinese journal included by major domestic retrieval systems, and an authoritative journal in image graphics and related fields.

It mainly publishes innovative and high-level academic papers on basic research and applied research in image and graphics science and its closely related fields.

The papers mainly include reviews, technical reports, project progress, academic trends, new technology reviews, new Product introduction and industrialization research, etc.

The content involves many fields such as image analysis and recognition, image understanding and computer vision, computer graphics, virtual reality and augmented reality, system simulation, animation, etc.

At the same time, corresponding thematic columns are set up according to the research hotspots and frontier topics of each period.

View Aims & Scope

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Metrics & Ranking

SJR (SCImago Journal Rank)

Year	Value
2024	0.222

Quartile

Year	Value
2024	Q3

h-index

Year	Value
2024	21

Journal Rank

Year	Value
2024	19958

Journal Citation Indicator

Year	Value
2024	1229

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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

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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.

• Deep learning-based image fusion: a survey

  Citation: 60

  Authors: Linfeng, Hao, Han, Jiayi

• Overview of deep convolutional neural networks for image classification

  Citation: 35

  Authors: Ke, Xiaohan, Yurong, Yukun, Kai, Zhenbing, Zhanyu, Qiaolin

• Survey on deep learning object detection

  Citation: 30

  Authors: Yongqiang, Yuan, Shipeng, Junyi

• An Adaptive Edge-detection Method Based on Canny Algorithm

  Citation: 28

  Authors:

• Review of data augmentation for image in deep learning

  Citation: 26

  Authors: Dongao, Ping, Lijun, Zheng

• Research of Measurement for Digital Image Definition

  Citation: 26

  Authors:

• Overview of the development and application of 3D vision measurement technology

  Citation: 24

  Authors: Zonghua, Wei, Guodong, Limei, Yufu, Xudong, Zhenzhong

• Overview of visual defect detection of transmission line components

  Citation: 21

  Authors: Zhenbing, Zhigang, Yanxu, Yincheng, Yongjie, Wenqing, Ke

• A survey on deep learning based visual object detection

  Citation: 19

  Authors: Jiale, Yali, Hanqing, Jin, Kaiqi, Yanwei

• The review of distortion-related image quality assessment

  Citation: 19

  Authors: Jiebin, Yuming, Xuelin

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