Data Mining and Algorithms Applied in Image Processing
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 11
Special Issue Editors
Interests: image processing; tensor learning; fast algorithm
Interests: signal processing; image processing; deep learning
Special Issue Information
Dear Colleagues,
Recent advances in mathematical modeling and learning algorithms are reshaping high‑dimensional imaging across multi-/hyperspectral fusion, remote sensing, and multi‑contrast MRI. This Special Issue seeks contributions that connect rigorous mathematics—including low‑rank models, transform‑domain and learnable priors, variational and inverse‑problem formulations, and optimization methods (e.g., ADMM, plug‑and‑play, implicit/explicit deep unfolding)—with scalable data‑driven architectures such as attention/Mamba‑style operators, neural operators, and diffusion models. We welcome works spanning theoretical guarantees (e.g., convergence, stability, identifiability), algorithmic innovations with interpretable structures, and application‑oriented studies on HSI/MSI fusion and super‑resolution, inpainting and completion with nonlocal/self‑similar priors, robust PCA and structured low‑rank recovery, change detection and semantic segmentation under distribution shift, and physics‑guided MRI reconstruction.
Prof. Dr. Jianwei Zheng
Guest Editor
Dr. Honghui Xu
Dr. Jiawei Jiang
Guest Editor Assistants
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Keywords
- low‑rank tensor modeling
- cascade/learnable transforms
- HSI/MSI fusion and super‑resolution
- nonlocal/self‑similar priors
- deep unfolding / plug‑and‑play
- neural operators / Mamba‑style operators
- diffusion models for imaging
- MRI reconstruction (multi‑contrast / multi‑sequence)
- remote sensing change detection
- semantic segmentation
- optimization (ADMM, PnP)
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