IEEE JSTSP Special Issue on High-Dimensional Imaging: Emerging Challenges and Advances in Reconstruction and Restoration

Manuscript Due: 31 May 2025
Publication Date: December 2025

High-dimensional imaging has become a rapidly evolving frontier in signal processing, with applications in autonomous systems, industrial manufacturing, healthcare, remote sensing, security, and robotics. These imaging modalities, such as video, hyperspectral, multispectral, dynamic microscopy, multi-view, and 3D imaging, enable richer visual representations but introduce significant challenges in reconstruction, restoration, and real-time deployment. Unlike traditional imaging systems, high-dimensional vision data incorporate additional spectral, temporal, polarization, or depth information, complicating acquisition, processing, and interpretation.

Despite advancements in physics-informed models, sparsity and low-rank regularization, deep learning architectures (e.g., Transformers, diffusion models, and LLMs), several key challenges remain. These include developing scalable and real-time methods for large-scale high-dimensional data, ensuring robustness against diverse noise types and sensor artifacts, integrating physics-based priors with AI-driven models for enhanced interpretability, and addressing real-world deployment constraints in industrial and scientific applications.

This special issue aims to showcase the latest achievements in high-dimensional reconstruction and restoration, while addressing key open issues and exploring promising new directions. In addition to advancing theoretical research, this special issue actively seeks to attract contributions from industry to bridge the gap between academic research and real-world applications. We encourage submissions from both academia and industry, with a special emphasis on industry-driven solutions, real-time deployment of high-dimensional imaging algorithms, and AI-powered industrial imaging applications. This special issue seeks high-quality submissions that tackle these open challenges, providing a balanced focus on theoretical foundations, algorithmic innovations, and real-world applications.

Topics of interest include, but are not limited to:

  • Physics-informed and Model-Based High-Dimensional Image Reconstruction
    • Hybrid models combining physics as well as mathematical priors with data-driven and generative AI approaches
    • Optimization techniques for high-dimensional image restoration with theoretical guarantees
    • Model-driven regularization methods for robust, interpretable, and computationally efficient imaging
  • Scalable and Real-Time Processing of Large-Scale High-Dimensional Data
    • Computationally efficient inverse problem-solving methods for real-time applications
    • Low-latency and energy-efficient algorithms for high-dimensional data processing
    • Multimodal fusion for heterogeneous imaging modalities (e.g., hyperspectral-3D fusion)
  • AI-Driven Imaging Advances, including Transformers, Diffusion Models, and Multimodal Learning
    • Vision Transformers, diffusion models, and large-scale AI architectures for high-dimensional imaging
    • Self-supervised and few-shot learning for data-efficient high-dimensional imaging tasks
    • AI-powered multimodal fusion and cross-domain learning for real-world applications (e.g., medical imaging, robotics, autonomous systems)
  • Benchmarking, Quality Assessment, and Industrial Deployment Strategies
    • Standardized evaluation metrics and large-scale datasets for high-dimensional imaging tasks
    • Robustness, interpretability, and scalability in real-world imaging applications
    • Challenges and case studies in deploying high-dimensional imaging solutions in industry, healthcare, and remote sensing

Important Dates

  • Manuscript submission due: 31 May 2025
  • First review completed: 30 June 2025
  • Revised manuscript due: 15 August 2025
  • Second review completed: 15 September 2025
  • Final manuscript due: 30 September 2025

Guest Editors

​For further information, please contact the guest editor: 

  • Prof. Zhiyuan Zha (Lead Guest Editor), Jilin University, China
  • Prof. Saiprasad Ravishankar, Michigan State University, USA
  • Dr. Chen Zhao, King Abdullah University of Science and Technology, Saudi Arabia
  • Dr. Hassan Mansour, Mitsubishi Electric Research Laboratories, USA
  • Prof. Giuseppe Valenzise, Université Paris-Saclay, France
  • Prof. Ce Zhu, University of Electronic Science and Technology of China, China
  • Prof. Alex C. Kot, Nanyang Technological University, Singapore
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JSTSP Special Issue
JSTSP Call for Papers