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Advances in Image Processing and Analysis
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Advances in Image Processing and Analysis

A Special Issue of Mathematics (ISSN 2227-7390) belonging to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 30 April 2027 | Viewed by 1849

Editor

Dr. Xiaofei Yang

E-Mail Website
Guest Editor
School of Electronics and Communications Engineering, Guangzhou University, Guangzhou 510006, China
Interests: image processing and analysis; computer vision and pattern recognition; hyperspectral remote sensing image interpretation; deep learning, visual state space models, and intelligent perception; multi-scale spatial-spectral feature representation and multimodal fusion; robust, explainable, and deployable AI for engineering visual applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Image processing and analysis continue to shape modern intelligent sensing, scientific discovery, and engineering decision-making. With the rapid growth of high-resolution, multimodal, and real-time visual data, the field is moving from task-specific algorithms toward robust, interpretable, and deployable visual intelligence. Important challenges remain in degradation-resilient restoration, fine-grained segmentation, small-sample recognition, cross-domain generalization, uncertainty-aware inference, and computationally efficient deployment on edge devices.

This Special Issue invites original research and high-quality reviews on advanced theories, models, and applications in image processing and analysis. Topics include, but are not limited to, image enhancement, restoration and super-resolution; object detection, semantic segmentation and scene understanding; medical, remote sensing, industrial and low-altitude vision; spectral–spatial representation learning; multimodal image fusion; graph-, geometry-, frequency- and physics-informed visual modeling; generative models, foundation models, transformers and state space models for visual data; lightweight architectures and edge inference; explainable, trustworthy, and uncertainty-aware visual analytics.

We particularly welcome studies that connect algorithmic innovation with rigorous evaluation, reproducible implementation, and practical deployment. Contributions may address theoretical foundations, benchmark studies, application-driven systems, or comprehensive surveys that clarify emerging trends in image processing and analysis.

Dr. Xiaofei Yang
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • image processing
  • image analysis
  • computer vision
  • deep learning
  • multimodal image fusion
  • image restoration
  • object detection
  • semantic segmentation
  • remote sensing
  • explainable AI

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Published Papers (5 papers)

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Research

38 pages, 26606 KB  
Article
LGScanNet: Gated Local–Global Selective Scanning for Joint Low-Light Enhancement and Deblurring
by Dongseong Moon, Hangmin Jo and Yong Ju Jung
Mathematics 2026, 14(17), 3081; https://doi.org/10.3390/math14173081 - 27 Aug 2026
Viewed by 322
Abstract
Images captured in low-light environments often suffer from coupled degradations, including insufficient illumination, amplified noise, and motion blur caused by long exposure: low illumination weakens the structural cues needed for deblurring, while blur further disperses already degraded edges and textures. Existing decoders for [...] Read more.
Images captured in low-light environments often suffer from coupled degradations, including insufficient illumination, amplified noise, and motion blur caused by long exposure: low illumination weakens the structural cues needed for deblurring, while blur further disperses already degraded edges and textures. Existing decoders for this joint restoration problem remain largely convolutional, propagating distant blur information only indirectly through repeated local operations, whereas directly substituting a state-space or attention module risks overwriting locally reliable structure with global context. This motivates a decoder that preserves local detail reconstruction while selectively admitting long-range directional dependencies. We propose LGScanNet, a gated local–global selective scanning network for joint low-light enhancement and deblurring. LGScanNet retains the illumination-oriented encoder of DarkIR and redesigns its deblurring decoder around the proposed Local–Global Deblurring Block (LGDB), which combines a Di-SpAM-based local detail branch with an Affine-Calibrated Multi-Head Selective Scan (AC-MHSS) branch. In AC-MHSS, shared affine calibration conditions four independently parameterized directional scan heads before they model direction-dependent blur trajectories. The local and global representations are then combined through Local-Guided Global Injection (LGGI), which anchors the fused feature on the local branch and admits the global response only through a learned gate and a channel-wise scale initialized near zero, so that global context is introduced gradually as a controlled correction to the local representation. Controlled comparisons against parameter-matched convolutional and state-space controls, together with component-wise ablations, indicate that the performance gains cannot be explained by increased model capacity or by selective scanning alone. On LOLBlur-Synthetic, LGScanNet-M improves our reproduced DarkIR-M baseline by approximately 0.70 dB in PSNR. Full article
(This article belongs to the Special Issue Advances in Image Processing and Analysis)
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15 pages, 22726 KB  
Article
SACMFuse: Structure-Aware Cross-Modal Interaction Network for Multi-Modal Image Fusion
by Quanrui Wen, Xiu Shu, Xinming Zhang and Di Yuan
Mathematics 2026, 14(16), 3018; https://doi.org/10.3390/math14163018 - 21 Aug 2026
Viewed by 284
Abstract
Multi-modal image fusion integrates complementary information from different modalities to generate a unified representation that is informative for human perception and beneficial to downstream vision tasks. However, existing methods often inefficiently model global features and their cross-modal interaction is insufficient, resulting in the [...] Read more.
Multi-modal image fusion integrates complementary information from different modalities to generate a unified representation that is informative for human perception and beneficial to downstream vision tasks. However, existing methods often inefficiently model global features and their cross-modal interaction is insufficient, resulting in the suboptimal preservation of structural details and modality-specific information. To address these issues, we propose SACMFuse, a structure-aware cross-modal interaction network for multi-modal image fusion. SACMFuse is built upon the linear complexity attention mechanism of LAMA, which enables efficient and effective global feature modeling. In this study, LAMA is extended into a CrossLAMA mechanism to facilitate deep-level information interaction among modalities. Within this framework, we propose a Frequency–Spatial Rectification Module (FSRM) that jointly models spatial-domain structures and frequency-domain representations in a unified manner. By perceiving and adaptively rectifying structural features across modalities, FSRM enhances the structural consistency and discriminability of the fused features. Furthermore, to strengthen cross-modal complementarity, we design a Difference-driven Cross-modal Interaction Module (DCIM), where inter-modal discrepancies explicitly guide information exchange among modalities. This mechanism encourages the network to focus on complementary structures while suppressing redundant responses. Extensive experiments on multiple datasets demonstrate that the performance of SACMFuse is competitive for the majority of metrics, providing a robust and advanced solution for image fusion tasks. Full article
(This article belongs to the Special Issue Advances in Image Processing and Analysis)
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35 pages, 20645 KB  
Article
Adaptive Temporal Reallocation and Trajectory-Aware Modulation for Event-Level Segmentation of Small-Scale UAVs
by Sunwoo Jang, Jaekyeong Choi and Yong Ju Jung
Mathematics 2026, 14(16), 2981; https://doi.org/10.3390/math14162981 - 18 Aug 2026
Viewed by 343
Abstract
Event-level segmentation of small-scale unmanned aerial vehicles (UAVs) is challenging because target-generated events are sparse and are easily mixed with background motion and sensor noise. This study extends EV-SpSegNet with Adaptive Temporal Reallocation (ATR), Trajectory-Aware Modulation (TAM), and Directional Mask Regularization (DMR). ATR [...] Read more.
Event-level segmentation of small-scale unmanned aerial vehicles (UAVs) is challenging because target-generated events are sparse and are easily mixed with background motion and sensor noise. This study extends EV-SpSegNet with Adaptive Temporal Reallocation (ATR), Trajectory-Aware Modulation (TAM), and Directional Mask Regularization (DMR). ATR reallocates the temporal coordinates used for sparse voxel construction according to interval-wise spatiotemporal linearity while preserving the original events, features, and labels. TAM combines x-t and y-t directional features and selectively modulates intermediate feature responses, while DMR discourages excessive positive modulation during training. On the EV-UAV benchmark, the proposed method improves the IoU of the reproduced EV-SpSegNet baseline from 57.10% to 65.74% and reduces Fa from 2.27×10−4 to 0.61×10−4. Additional evaluation on NeRDD, multi-seed training, and controlled ablation analyses further demonstrate the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Advances in Image Processing and Analysis)
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25 pages, 2171 KB  
Article
TBSA: Tri-Domain Balanced Spectral–Spatial Attention with Deformable Frequency Filtering for Hyperspectral Image Classification
by Shuzhuan Tang and Xiaofei Yang
Mathematics 2026, 14(15), 2754; https://doi.org/10.3390/math14152754 - 3 Aug 2026
Viewed by 302
Abstract
Hyperspectral image classification (HSIC) requires a classifier to distinguish land-cover categories from densely sampled spectral signatures while preserving the spatial arrangement of local materials. Although convolutional networks, Transformer architectures, and recent state-space models have greatly improved spectral–spatial representation learning, three issues remain insufficiently [...] Read more.
Hyperspectral image classification (HSIC) requires a classifier to distinguish land-cover categories from densely sampled spectral signatures while preserving the spatial arrangement of local materials. Although convolutional networks, Transformer architectures, and recent state-space models have greatly improved spectral–spatial representation learning, three issues remain insufficiently resolved. First, spectral redundancy and local spatial textures are commonly modeled in the original feature domain, where low- and high-frequency responses are only implicitly separated. Second, fixed or weakly adaptive frequency operations cannot reflect the fact that different land-cover classes rely on different spectral smoothness, boundary, and texture cues. Third, spatial evidence, channel selectivity, and frequency responses are often fused by a uniform rule, which may be suboptimal under limited training samples and class imbalance. To address these issues, this paper proposes Tri-Domain Balanced Spectral–Spatial Attention(TBSA), a compact frequency-aware framework for HSIC. TBSA projects intermediate features into one-dimensional spectral, two-dimensional spatial, and three-dimensional spectral–spatial discrete cosine transform (DCT) domains, and it introduces a deformable frequency filter to adaptively separate low- and high-frequency components. Spatial–frequency and spatial–channel interaction form complementary evidence, while the final aggregation rule controls the balance between input-conditioned flexibility, numerical stability, and parameter cost. Experiments on Indian Pines, Houston 2013, and WHU-Hi-LongKou show competitive mean OA and strong class-wise or balanced-accuracy behavior. A five-run inferential analysis does not establish statistically significant OA superiority over the closest DCTN baseline, and the claims are therefore restricted to the observed mean and class-wise results. Full article
(This article belongs to the Special Issue Advances in Image Processing and Analysis)
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23 pages, 4765 KB  
Article
Nonlocal Low-Rank Residual Modeling for Hyperspectral Image Mixed Noise Removal
by Lixia Xia, Youqun Chen, Xin Wang and Hongbing Sun
Mathematics 2026, 14(15), 2731; https://doi.org/10.3390/math14152731 - 1 Aug 2026
Viewed by 252
Abstract
Hyperspectral image (HSI) denoising remains a critical challenge due to noise corruption during acquisition. While nonlocal low-rank (LR) tensor methods leverage spatial–spectral correlations, they usually fail under heavy or complex noise, as directly estimating LR tensors from noisy observations usually leads to residual [...] Read more.
Hyperspectral image (HSI) denoising remains a critical challenge due to noise corruption during acquisition. While nonlocal low-rank (LR) tensor methods leverage spatial–spectral correlations, they usually fail under heavy or complex noise, as directly estimating LR tensors from noisy observations usually leads to residual noise accumulation. To address this limitation, we propose a novel nonlocal low-rank residual (NLRR) approach, which reformulates LR tensor recovery as a progressive residual minimization problem. Unlike conventional methods that exclusively approximate LR tensors directly from degraded observations, the proposed NLRR approach iteratively refines the latent LR tensor structure by minimizing the rank residual, thereby decoupling noise suppression from tensor approximation. This residual-driven framework uniquely integrates two complementary priors: (1) a nonlocal LR residual prior that exploits spatial self-similarity, and (2) a global spectral LR prior that suppresses spectral redundancy. To generalize the proposed NLRR approach to real-world scenarios with mixed noise, we develop the NLRR-robust principal component analysis (NLRR-RPCA) framework, which incorporates the LR residual along with global spectral LR and sparse tensor priors for mixed noise removal. Additionally, to ensure both numerical stability and computational tractability, we develop an adaptive rank-adjusted alternating minimization algorithm, which dynamically adjusts the ranks of the estimated tensors to better handle different noise scenarios. Extensive experiments on both simulated and real HSI datasets demonstrate that our proposed NLRR approach outperforms numerous popular or state-of-the-art methods in both quantitative evaluation and visual perception. Full article
(This article belongs to the Special Issue Advances in Image Processing and Analysis)
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