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 1743

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

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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 (2 papers)

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Research

21 pages, 25189 KiB  
Article
Imaginique Expressions: Tailoring Personalized Short-Text-to-Image Generation Through Aesthetic Assessment and Human Insights
by Yitian Wan, Luwei Xiao, Xingjiao Wu, Jing Yang and Liang He
Symmetry 2024, 16(12), 1608; https://doi.org/10.3390/sym16121608 - 3 Dec 2024
Viewed by 381
Abstract
The text-to-image task, a critical branch of computer vision and image processing, has witnessed remarkable advancements fueled by the abundance of realistic data and rapid AI innovation. However, existing research often overlooks scenarios involving sparse textual input and fails to incorporate human personalized [...] Read more.
The text-to-image task, a critical branch of computer vision and image processing, has witnessed remarkable advancements fueled by the abundance of realistic data and rapid AI innovation. However, existing research often overlooks scenarios involving sparse textual input and fails to incorporate human personalized preferences into the generative process. To address these gaps, we propose a novel AI methodology: personalized short-text-to-image generation through aesthetic assessment and human insights. Our approach introduces a symmetry between personalized aesthetic preferences and the generated images by leveraging a data-driven personality encoder (PE) to extract personal information and embed it into a Big Five personality trait-based image aesthetic assessment (BFIAA) model. This model harmonizes aesthetic preferences with the generative process by adapting the stable diffusion framework to align with personalized assessments. Experimental results demonstrate the effectiveness of our method: the PE module achieves an accuracy of 98.1%, while the BFIAA model surpasses the baseline by 13% on the PLCC metric, accurately reflecting human aesthetic preferences. Furthermore, our adapted generation model improves convergence loss by over 10% compared to the base model, consistently producing personalized images that are more aligned with human preferences. Full article
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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 860
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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