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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 10
3166-3171,3195

Hyperspectral image restoration model with side information

Zhang Shaojie1,2,3
Luo Qiong1,2,3
Han Zhi1,2
Tang Yandong1,2
1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2. Institutes for Robotics & Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
DOI: 10.19734/j.issn.1001-3695.2020.12.0564

Abstract

For hyperspectral image(HSI) restoration, how to effectively embed prior information in the model and correctly model the noise have always been the two focus of research. As a domain-dependent prior knowledge, side information has succeeded in many aspects, but it has not received much attention in the field of hyperspectral denoising. In order to naturally couple this domain knowledge with the hyperspectral restoration model, the method linked side information to the underlying matrix representing the potential low-rank structure of the observation data via a bilinear mapping, and used E-3DTV to encode HSI local smoothness prior. In addition, this method used the Lp norm for noise modeling to further enhance the robustness against corruption. This method was compared with five competitive methods on three numerical indicators in two data sets and seven noise addition methods. The results fully reflect the effectiveness and universality of the proposed method for complex noise scene.

Foundation Support

国家自然科学基金资助项目(61903358)
国家自然科学基金面上项目(61773367)
国家自然科学基金创新群体项目(61821005)
中国科学院青年创新促进会资助项目(2016183)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.12.0564
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 10
Section: Technology of Graphic & Image
Pages: 3166-3171,3195
Serial Number: 1001-3695(2021)10-051-3166-06

Publish History

[2021-10-05] Printed Article

Cite This Article

张少杰, 罗琼, 韩志, 等. 基于边信息的高光谱图像恢复模型 [J]. 计算机应用研究, 2021, 38 (10): 3166-3171,3195. (Zhang Shaojie, Luo Qiong, Han Zhi, et al. Hyperspectral image restoration model with side information [J]. Application Research of Computers, 2021, 38 (10): 3166-3171,3195. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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