Model-Driven, Data-Driven and Symmetry Methods in Hyperspectral Image Processing

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 1205

Special Issue Editors


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Guest Editor
School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, China
Interests: hyperspectral image processing; computer vision; machine learning

E-Mail Website
Guest Editor
School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China
Interests: tensor modeling and computing; tensor learning; hyperspectral image processing

Special Issue Information

Dear Colleagues,

Image and spectra are the two essential bases that people use to recognize and distinguish between objects in the real world. Images provide a basis to solve geometric problems related to geographical objects, and spectra reflect the unique physical properties of these geographical objects. Hyperspectral images (HSIs) play an increasingly important role in various fields, such as remote sensing, object detection, and medical examination. In recent years, model-driven, data-driven, and symmetry technologies have attracted much attention with regard to the field of HSI processing. This Special Issue aims to discuss new model-driven, data-driven, and symmetry methods that can solve problems related to HSI processing. By launching this Special Issue, we hope to promote the development of corresponding models and algorithms. Therefore, researchers who work in areas related to these research fields are encouraged to contribute papers for publication in this Special Issue.

Dr. Yong Chen
Dr. Yu-Bang Zheng
Guest Editors

Manuscript Submission Information

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Keywords

  • remote sensing
  • multispectral/hyperspectral image processing
  • restoration, reconstruction, and fusion
  • saliency detection and anomaly detection
  • application in remote sensing
  • optimization modeling
  • machine learning and deep learning
  • symmetry

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Published Papers (1 paper)

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Research

19 pages, 312 KiB  
Article
Modified Double Inertial Extragradient-like Approaches for Convex Bilevel Optimization Problems with VIP and CFPP Constraints
by Yue Zeng, Lu-Chuan Ceng, Liu-Fang Zheng and Xie Wang
Symmetry 2024, 16(10), 1324; https://doi.org/10.3390/sym16101324 - 8 Oct 2024
Viewed by 779
Abstract
Convex bilevel optimization problems (CBOPs) exhibit a vital impact on the decision-making process under the hierarchical setting when image restoration plays a key role in signal processing and computer vision. In this paper, a modified double inertial extragradient-like approach with a line search [...] Read more.
Convex bilevel optimization problems (CBOPs) exhibit a vital impact on the decision-making process under the hierarchical setting when image restoration plays a key role in signal processing and computer vision. In this paper, a modified double inertial extragradient-like approach with a line search procedure is introduced to tackle the CBOP with constraints of the CFPP and VIP, where the CFPP and VIP represent a common fixed point problem and a variational inequality problem, respectively. The strong convergence analysis of the proposed algorithm is discussed under certain mild assumptions, where it constitutes both sections that possess a mutual symmetry structure to a certain extent. As an application, our proposed algorithm is exploited for treating the image restoration problem, i.e., the LASSO problem with the constraints of fractional programming and fixed-point problems. The illustrative instance highlights the specific advantages and potential infect of the our proposed algorithm over the existing algorithms in the literature, particularly in the domain of image restoration. Full article
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