Journal of Imaging

17 pages, 5235 KB

Open AccessArticle

An Effective Non-Rigid Registration Approach for Ultrasound Images Based on the Improved Variational Model of Intensity, Local Phase Information and Descriptor Matching

by Kun Zhang, Jinming Xing and Qingtai Xiao

J. Imaging 2026, 12(4), 156; https://doi.org/10.3390/jimaging12040156 - 3 Apr 2026

Abstract

Ultrasound images have some limitations, such as low signal-to-noise ratio (SNR), speckle noise, lower dynamic range, blurred boundaries, and shadowing; therefore, ultrasound image registration is an important task for estimating tissue motion and analyzing tissue mechanical properties. In this paper, an effective non-rigid [...] Read more.

Ultrasound images have some limitations, such as low signal-to-noise ratio (SNR), speckle noise, lower dynamic range, blurred boundaries, and shadowing; therefore, ultrasound image registration is an important task for estimating tissue motion and analyzing tissue mechanical properties. In this paper, an effective non-rigid ultrasound image registration method is proposed. By integrating intensity, local phase information, and descriptor matching under a variational framework, we can find and track the non-rigid transformation of each pixel under diffeomorphism between the source and target images based on the warping technique. Experiments using simulation and in vivo ultrasound images of the human carotid artery are conducted to demonstrate the advantages of the proposed algorithm, which will act as an important supplement to current ultrasound image registration. Full article

(This article belongs to the Section Image and Video Processing)

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18 pages, 1365 KB

Open AccessArticle

DA-CycleGAN: Degradation-Adaptive Unpaired Super-Resolution for Historical Image Restoration

by Lujun Zhai, Yonghui Wang, Yu Zhou and Suxia Cui

J. Imaging 2026, 12(4), 155; https://doi.org/10.3390/jimaging12040155 - 3 Apr 2026

Abstract

Historical images as the dominant method for documenting the world and its inhabitants can help us to better understand the real history. Due to the limited camera technology, historical images captured in the early to mid-20th century tend to be very blurry, unclear, [...] Read more.

Historical images as the dominant method for documenting the world and its inhabitants can help us to better understand the real history. Due to the limited camera technology, historical images captured in the early to mid-20th century tend to be very blurry, unclear, noisy, and obscure. The goal of this paper is to super-resolve images for historical image restoration. Compared to the degradations in modern digital imagery, those in historical images have unique features that are typically much more complex and less well understood. The discrepancy between historical images and modern high-definition digital images leads to a significant performance drop for existing super-resolution (SR) models trained on modern digital imagery. To tackle this problem, we propose a new method, namely DA-CycleGAN. Specifically, the DA-CycleGAN is built on top of CycleGAN to achieve unsupervised learning. We introduce a degradation-adaptive (DA) module with strong, flexible adaptation to learn various unknown degradations from samples. Moreover, we collect a large dataset containing 10,000 low-resolution images from real historical films. The dataset features various natural degradations. Our experimental results demonstrate the superior performance of DA-CycleGAN and the effectiveness of our image dataset for achieving accurate super-resolution enhancement of historical images. Full article

(This article belongs to the Section Computer Vision and Pattern Recognition)

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26 pages, 6199 KB

Open AccessArticle

WeatherMAR: Complementary Masking of Paired Tokens for Adverse-Weather Image Restoration

by Junyuan Ma, Qunbo Lv and Zheng Tan

J. Imaging 2026, 12(4), 154; https://doi.org/10.3390/jimaging12040154 - 2 Apr 2026

Abstract

Image restoration under adverse weather conditions has attracted increasing attention because of its importance for both human perception and downstream vision applications. Existing methods, however, are often designed for a single degradation type. We present WeatherMAR, a multi-weather restoration framework that formulates [...] Read more.

Image restoration under adverse weather conditions has attracted increasing attention because of its importance for both human perception and downstream vision applications. Existing methods, however, are often designed for a single degradation type. We present WeatherMAR, a multi-weather restoration framework that formulates adverse-weather restoration as a paired-domain completion problem in a shared continuous token space. Specifically, WeatherMAR concatenates degraded and clean token sequences into a joint paired-domain sequence and performs restoration through masked autoregressive modeling, in which self-attention enables direct cross-domain interaction. To strengthen conditional learning while avoiding trivial paired correspondences, we introduce complementary bidirectional masking together with an optional reverse objective used only during training to encourage degradation-aware representations. WeatherMAR further employs a conditional diffusion objective for continuous token prediction and adopts a progress-to-step schedule to improve inference efficiency. Extensive experiments on standard multi-weather benchmarks, including Snow100K, Outdoor-Rain, and RainDrop, show that WeatherMAR achieves the best PSNR/SSIM on Snow100K-S (38.14/0.9684), the best SSIM on Outdoor-Rain (0.9396), and the best PSNR on Snow100K-L (32.58) and RainDrop (33.12). These results demonstrate that paired-domain token completion provides an effective solution for adverse-weather restoration. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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42 pages, 10149 KB

Open AccessArticle

Radon-Guided Wavelet-Domain Attention U-Net for Periodic Artifact Suppression in Brain MRI

by Jesus David Rios-Perez, German Sanchez-Torres, John W. Branch-Bedoya and Camilo Andres Laiton-Bonadiez

J. Imaging 2026, 12(4), 153; https://doi.org/10.3390/jimaging12040153 - 2 Apr 2026

Abstract

Periodic artifacts such as ringing (Gibbs), herringbone (spike/corduroy), and zipper patterns degrade the quality of brain MRI. We present a reproducible framework that (i) synthetically generates periodic artifacts with controllable severity directly in k-space, (ii) normalizes pattern orientation through a Radon-guided alignment step, [...] Read more.

Periodic artifacts such as ringing (Gibbs), herringbone (spike/corduroy), and zipper patterns degrade the quality of brain MRI. We present a reproducible framework that (i) synthetically generates periodic artifacts with controllable severity directly in k-space, (ii) normalizes pattern orientation through a Radon-guided alignment step, and (iii) corrects them in the wavelet domain using a 2D DWT (AA/AD/DA/DD) with a band-weighted loss. The evaluation was conducted using DLBS T1-weighted 3T MRI volumes with synthetically generated periodic artifacts. It combined global image-quality metrics (SSIM, PSNR) with per-band metrics to quantify how correction concentrates on high-frequency components, and included ablation studies, mixed-artifact stress tests, and structural preservation analyses. Compared with several baseline architectures, the proposed approach shows improvements in structural fidelity and a reduction in periodic patterns (SSIM:

0.985 \pm 0.022;

PSNR:

43.337 \pm 5.364

; reduction in concentrated error in high-frequency bands), while preserving unaffected structures. These findings indicate that, within a controlled synthetic benchmark, aligning the pattern orientation prior to learning and optimizing correction in the wavelet domain enables suppression of synthetically generated periodic artifacts while limiting over-smoothing. Full article

(This article belongs to the Section Medical Imaging)

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25 pages, 15197 KB

Open AccessArticle

Semi-Automated Computational Identification of Fibrosis for Enhanced Histopathological Decision Support

by Alexandru-George Berciu, Diana Rus-Gonciar, Teodora Mocan, Lucia Agoston-Coldea, Carmen Cionca and Eva-Henrietta Dulf

J. Imaging 2026, 12(4), 152; https://doi.org/10.3390/jimaging12040152 - 31 Mar 2026

Abstract

Myocardial fibrosis is a critical prognostic marker involving a progressive cascade of pathological conditions. Accurate assessment of fibrosis in myocardial samples is a routine but difficult procedure for pathologists. This article presents a semi-automated system designed to ease this task while providing pixel-level [...] Read more.

Myocardial fibrosis is a critical prognostic marker involving a progressive cascade of pathological conditions. Accurate assessment of fibrosis in myocardial samples is a routine but difficult procedure for pathologists. This article presents a semi-automated system designed to ease this task while providing pixel-level accuracy that exceeds manual estimation capabilities. The proposed innovative approach combines Gabor filters with CIELAB color space analysis to ensure the efficiency and interpretability of calculations. Testing on histopathological samples, differentiating between fibrous, healthy, and variant tissues, yielded a promising accuracy of 87.5% for images with fibrosis and 80% for all 45 images tested. This system successfully establishes a solid foundation for automated diagnosis, providing pathologists with a reliable and highly accurate tool for quantitative analysis of cardiac tissue. Full article

(This article belongs to the Section AI in Imaging)

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15 pages, 1260 KB

Open AccessArticle

Radiomic Characterization of Adrenal Incidentalomas on NECT: Retrospective Exploratory Study and Systematic Review

by Pasquale Frisina, Paolo Ricci, Filippo Valentini and Daniela Messineo

J. Imaging 2026, 12(4), 151; https://doi.org/10.3390/jimaging12040151 - 30 Mar 2026

Abstract

Radiomics may aid the noninvasive characterization of adrenal incidentalomas; however, reproducibility is limited by methodological heterogeneity. In this retrospective, single-center, exploratory study, we tested whether radiomic features from baseline non-enhanced computed tomography (NECT) discriminate benign from malignant/metastatic adrenal lesions and contextualized results with [...] Read more.

Radiomics may aid the noninvasive characterization of adrenal incidentalomas; however, reproducibility is limited by methodological heterogeneity. In this retrospective, single-center, exploratory study, we tested whether radiomic features from baseline non-enhanced computed tomography (NECT) discriminate benign from malignant/metastatic adrenal lesions and contextualized results with a PRISMA 2020 systematic review (PubMed/Scopus 2017–2025; PROSPERO CRD420251276627). Thirty-three patients (36 lesions: 12 lipid-rich adenomas, 9 lipid-poor adenomas, 6 pheochromocytomas, 7 malignant/metastatic lesions, 2 myelolipomas) were included; myelolipomas were excluded from primary comparisons. Two abdominal radiologists performed consensus 3D segmentation on NECT. Using LIFEx (v7.8.0) and IBSI definitions, 42 features were extracted and z-score standardized. LASSO selected four heterogeneity descriptors: First-order Entropy, gray-level co-occurrence matrix (GLCM) entropy, gray-level size zone matrix (GLSZM) non-uniformity, and neighboring gray tone difference matrix (NGTDM) busyness. Heterogeneity increased from lipid-rich adenomas to pheochromocytomas and malignant/metastatic lesions (Kruskal–Wallis, all p < 0.001. Pairwise separability, measured using the Vargha–Delaney A index (VDA) as a rank-based measure of separability, was highest for lipid-rich adenomas versus malignant/metastatic lesions (0.93), intermediate for lipid-poor adenomas versus pheochromocytomas (0.73), and lowest for lipid-rich versus lipid-poor adenomas (0.64). The review identified 18 eligible CT radiomics studies that consistently reported higher entropy/non-uniformity in pheochromocytomas and malignant lesions than in lipid-rich adenomas. Global heterogeneity metrics on NECT may complement conventional CT criteria in indeterminate lesions; external validation with robust reference standards is needed in larger, multicenter cohorts with harmonization. Full article

(This article belongs to the Special Issue Tools and Techniques for Improving Radiological Imaging Applications)

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23 pages, 3504 KB

Open AccessArticle

Spatially Time-Based Robust Tracking and Re-Identification of Kindergarten Students: A Hybrid Deep Learning Framework Combining YOLOv8n and Vision Transformer (ViT)

by Md. Rahatul Islam, Yui Kataoka, Keisuke Teramoto and Keiichi Horio

J. Imaging 2026, 12(4), 150; https://doi.org/10.3390/jimaging12040150 - 30 Mar 2026

Abstract

Detection, tracking, and re-identification (ReID) of children wearing similar uniforms in a kindergarten environment is a very complex challenge for computer vision. Traditional surveillance systems or simple convolutional neural network (CNN) models often fail to distinguish children in crowds and occlusions. To address [...] Read more.

Detection, tracking, and re-identification (ReID) of children wearing similar uniforms in a kindergarten environment is a very complex challenge for computer vision. Traditional surveillance systems or simple convolutional neural network (CNN) models often fail to distinguish children in crowds and occlusions. To address this challenge, this study proposes a novel hybrid framework combining YOLOv8 and Vision Transformer (ViT). Using YOLOv8 for detection and ViT for global feature extraction, we trained the model on a custom dataset of 31,521 images, achieving an overall accuracy of 93.75%, and the public benchmark MOT20 dataset of 28,630 images, achieving an overall accuracy of 96.02%. Our system showed remarkable success in tracking performance, where it achieved 86.7% MOTA and 99.7% IDF1 scores. This high IDF1 score proves that the model is highly effective in preventing identity switch. The main novelty of this study is the behavioral analysis of children beyond the boundaries of surveillance, where we measure walking distance and trajectory, and screen time. Finally, through cross-dataset comparison with the MOT20 public benchmark, we demonstrated that our proposed customized model is much more effective than current state-of-the-art methods in overcoming the domain gap in specific environments such as kindergarten. Full article

(This article belongs to the Special Issue Recent Progress and Challenges in Computer Vision and Machine Learning)

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30 pages, 21910 KB

Open AccessArticle

A New Feature Set for Texture-Based Classification of Remotely Sensed Images in a Quantum Framework

by Archana G. Pai, Koushikey Chhapariya, Krishna M. Buddhiraju and Surya S. Durbha

J. Imaging 2026, 12(4), 149; https://doi.org/10.3390/jimaging12040149 - 30 Mar 2026

Abstract

Texture feature extraction plays a crucial role in land-use and land-cover (LULC) classification for the remotely sensed images. However, when these images are quantized to a limited number of gray levels to reduce data volume or noise, conventional texture descriptors often lose discriminative [...] Read more.

Texture feature extraction plays a crucial role in land-use and land-cover (LULC) classification for the remotely sensed images. However, when these images are quantized to a limited number of gray levels to reduce data volume or noise, conventional texture descriptors often lose discriminative power. This study investigates singular values of the gray-level co-occurrence matrix (GLCM) as novel texture features for image classification, with local binary pattern (LBP), complete LBP (CLBP) statistics, and original GLCM features proposed by Haralick et al. for comparison. Under coarse quantization, texture descriptors of LBP and its variants, which encode micro-texture, lose detail, whereas GLCM, which encodes macro-texture, retains structural co-occurrence patterns. This study thus proposes a new feature set, namely the Singular Values of the gray-level co-occurrence matrix (SVGM), for texture discrimination. Experimental analysis indicates SVGM achieves higher class separability by preserving dominant spatial structure while suppressing noise and redundancy. Quantitative evaluation using classical SVMs with multiple kernels, quantum learning models with different kernels, and neural baselines (ANN and 1D-CNN) further shows that SVGM consistently improves classification performance. Within our tested models, quantum kernel SVMs are competitive and achieve the best results on some datasets, while classical models perform best on others. Full article

(This article belongs to the Section Image and Video Processing)

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17 pages, 5158 KB

Open AccessArticle

Quantifying Light Harshness: Method Automation and Influence of Photographic Light Modifiers

by Veronika Štampfl and Jure Ahtik

J. Imaging 2026, 12(4), 148; https://doi.org/10.3390/jimaging12040148 - 27 Mar 2026

Abstract

Accurate assessment of light properties is essential and is measured with photometric and colorimetric standardized methods. However, the spatial characteristic of light—harshness—remains difficult to quantify. Building on the authors’ previous work, this study presented a fully automated method for determining light source harshness [...] Read more.

Accurate assessment of light properties is essential and is measured with photometric and colorimetric standardized methods. However, the spatial characteristic of light—harshness—remains difficult to quantify. Building on the authors’ previous work, this study presented a fully automated method for determining light source harshness based on image analysis of cast shadows in a standardized environment. The improved method eliminated the need for manual shadow segmentation by introducing algorithmic noise removal and adaptive smoothing of shadow data. The method was applied to 180 test images comprising 30 combinations of photographic light-shaping attachments (e.g., softboxes, beauty dishes, and snoots) across two light sources (halogen and xenon) and three intensity levels. The results showed that the method was capable of detecting subtle differences in shadow properties and confirmed the influence of geometry, material, and orientation of the light modifiers on harshness. In addition, the results provided quantitative insight into the influence of photographic light modifiers on the original light. Full article

(This article belongs to the Section Image and Video Processing)

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33 pages, 783 KB

Open AccessSystematic Review

A Systematic Review of Deep Learning Approaches for Hepatopancreatic Tumor Segmentation

by Razeen Hussain, Muhammad Mohsin, Dadan Khan and Mohammad Zohaib

J. Imaging 2026, 12(4), 147; https://doi.org/10.3390/jimaging12040147 - 26 Mar 2026

Abstract

Deep learning has advanced rapidly in medical image segmentation, yet hepatopancreatic tumor delineation remains challenging due to low contrast, small lesion size, organ variability, and limited high-quality annotations. Existing reviews are outdated or overly broad, leaving recent architectural developments, training strategies, and dataset [...] Read more.

Deep learning has advanced rapidly in medical image segmentation, yet hepatopancreatic tumor delineation remains challenging due to low contrast, small lesion size, organ variability, and limited high-quality annotations. Existing reviews are outdated or overly broad, leaving recent architectural developments, training strategies, and dataset limitations insufficiently synthesized. To address this gap, we conducted a PRISMA 2020 systematic literature review of studies published between 2021 and 2026 on deep learning-based liver and pancreatic tumor segmentation. From 2307 records, 84 studies met inclusion criteria. U-Net variants continue to dominate, achieving strong liver segmentation but inconsistent tumor accuracy, while transformer-based and hybrid models improve global context modeling at higher computational cost. Attention mechanisms, boundary-refinement modules, and semi-supervised learning offer incremental gains, yet pancreatic tumor segmentation remains notably difficult. Persistent issues, including domain shift, class imbalance, and limited generalization across datasets, underscore the need for more robust architectures, standardized benchmarks, and clinically oriented evaluation. This review consolidates recent progress and highlights key challenges that must be addressed to advance reliable hepatopancreatic tumor segmentation. Full article

(This article belongs to the Section Medical Imaging)

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29 pages, 4764 KB

Open AccessArticle

A Two-Level Illumination Correction Network for Digital Meter Reading Recognition in Non-Uniform Low-Light Conditions

by Haoning Fu, Zhiwei Xie, Wenzhu Jiang, Xingjiang Ma and Dongying Yang

J. Imaging 2026, 12(4), 146; https://doi.org/10.3390/jimaging12040146 - 25 Mar 2026

Abstract

The automatic reading recognition of digital instruments is crucial for achieving metering automation and intelligent inspection. However, in non-standardized industrial environments, the masking effect caused by the coupling of non-uniform low-light conditions and the reflective surfaces of instrument panels severely degrades the displayed [...] Read more.

The automatic reading recognition of digital instruments is crucial for achieving metering automation and intelligent inspection. However, in non-standardized industrial environments, the masking effect caused by the coupling of non-uniform low-light conditions and the reflective surfaces of instrument panels severely degrades the displayed information, significantly limiting the recognition performance. Conventional image processing methods, while aiming to restore the imaging quality of instrument panels through low-light enhancement, inevitably introduce overexposure and indiscriminately amplify background noise during this process. To address the two key challenges of illumination recovery and noise suppression in the process of restoring panel image quality under non-uniform low-light conditions, this paper proposes a coarse-to-fine cascaded perception framework (CFCP). First, a lightweight YOLOv10 detector is employed to coarsely localize the meter reading region under non-uniform illumination conditions. Second, an Adaptive Illumination Correction Module (AICM) is designed to decouple and correct the illumination component at the pixel level, effectively restoring details in dark areas. Then, an Illumination-invariant Feature Perception Module (IFPM) is embedded at the feature level to dynamically perceive illumination-invariant features and filter out noise interference. Finally, the refined detection results are fed into a lightweight sequence recognition network to obtain the final meter readings. Experiments on a self-built industrial digital instrument dataset show that the proposed method achieves 93.2% recognition accuracy, with 17.1 ms latency and only 7.9 M parameters. Full article

(This article belongs to the Special Issue AI-Driven Image and Video Understanding)

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27 pages, 9896 KB

Open AccessArticle

Refer-ASV: Referring Multi-Object Tracking in Autonomous Surface Vehicle Navigation Scenes

by Bin Xue, Qiang Yu, Kun Ding, Ying Wang, Shiming Xiang and Chunhong Pan

J. Imaging 2026, 12(4), 145; https://doi.org/10.3390/jimaging12040145 - 25 Mar 2026

Abstract

Water-surface perception is critical for autonomous surface vehicle navigation, where reliable tracking of task-relevant objects is essential for safe and robust operation. Referring multi-object tracking (RMOT) provides a flexible tracking paradigm by allowing users to specify objects of interest through natural language. However, [...] Read more.

Water-surface perception is critical for autonomous surface vehicle navigation, where reliable tracking of task-relevant objects is essential for safe and robust operation. Referring multi-object tracking (RMOT) provides a flexible tracking paradigm by allowing users to specify objects of interest through natural language. However, existing RMOT benchmarks are mainly designed for ground or satellite scenes and fail to capture the distinctive visual and semantic characteristics of water-surface environments, including strong reflections, severe illumination variations, weak motion constraints, and a high proportion of small objects. To address this gap, we introduce Refer-ASV, the first RMOT dataset tailored for ASV navigation in complex water-surface scenes. Refer-ASV is constructed from real-world ASV videos and features diverse navigation scenes and fine-grained vessel categories. To facilitate systematic evaluation on Refer-ASV, we further propose RAMOT, an end-to-end baseline framework that enhances visual–language alignment throughout the tracking pipeline by improving visual–language alignment and robustness in challenging maritime environments. Experimental results show that RAMOT achieves a HOTA score of 39.97 on Refer-ASV, outperforming existing methods. Additional experiments on Refer-KITTI demonstrate its generalization ability across different scenes. Full article

(This article belongs to the Section Computer Vision and Pattern Recognition)

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21 pages, 38078 KB

Open AccessArticle

Development and Evaluation of a Deep Learning Model for Ovarian Cancer Histotype Classification Using Whole-Slide Imaging

by Dagoberto Pulido and Nathalia Arias-Mendoza

J. Imaging 2026, 12(4), 144; https://doi.org/10.3390/jimaging12040144 - 25 Mar 2026

Abstract

The histopathological classification of ovarian carcinoma is fundamental for patient management. While microscopic evaluation by pathologists is the current diagnostic standard, it is known to be subject to interobserver variability, which can affect consistency in treatment decisions. This study addresses this clinical need [...] Read more.

The histopathological classification of ovarian carcinoma is fundamental for patient management. While microscopic evaluation by pathologists is the current diagnostic standard, it is known to be subject to interobserver variability, which can affect consistency in treatment decisions. This study addresses this clinical need by developing and validating a deep learning-based diagnostic support tool designed to enhance the objectivity and reproducibility of this classification. In this work, we address a key challenge in computational pathology—the tendency of attention mechanisms to overfit by concentrating on limited features—by systematically evaluating a direct regularization method within multiple instance learning (MIL) models. The models were trained and validated using 10-fold cross-validation on a public training set of 538 whole-slide images and further tested on an independent public dataset for the more challenging task of molecular subtype classification. We utilized features from a foundational model pre-trained on histopathology data to represent tissue morphology. Our findings demonstrate that directly regularizing the attention mechanism with a stochastic approach provides a statistically significant improvement in accuracy and generalization, highlighting its power as a robust technique to mitigate overfitting for this clinical task. In direct contrast to the reported variability in manual assessment, our final model achieved high consistency and accuracy, with a balanced accuracy of 0.854 and a Cohen’s Kappa of 0.791. The model also demonstrated strong generalization on the molecular classification task. Its attention mechanism provides visual heatmaps for pathologist review, fostering interpretability and trust. We have developed a highly accurate and generalizable artificial intelligence tool that directly addresses the challenge of interobserver variability in ovarian cancer classification. Its performance highlights the potential for artificial intelligence to serve as a decision support system, standardizing histopathological assessment. Full article

(This article belongs to the Topic Applied Computer Vision and Pattern Recognition: 2nd Edition)

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25 pages, 13685 KB

Open AccessArticle

Vision and Language Reference for a Segment Anything Model for Few-Shot Segmentation

by Kosuke Sakurai, Ryotaro Shimizu and Masayuki Goto

J. Imaging 2026, 12(4), 143; https://doi.org/10.3390/jimaging12040143 - 24 Mar 2026

Abstract

Segment Anything Model (SAM)-based few-shot segmentation models traditionally rely solely on annotated reference images as prompts, which inherently limits their accuracy due to an over-reliance on visual cues and a lack of semantic context. This reliance leads to incorrect segmentation, where visually similar [...] Read more.

Segment Anything Model (SAM)-based few-shot segmentation models traditionally rely solely on annotated reference images as prompts, which inherently limits their accuracy due to an over-reliance on visual cues and a lack of semantic context. This reliance leads to incorrect segmentation, where visually similar objects from different categories are incorrectly identified as the target object. We propose Vision and Language Reference Prompt into SAM (VLP-SAM), a novel few-shot segmentation model that integrates both visual information of reference images and semantic information of text labels into SAM. VLP-SAM introduces a vision-language model (VLM) with pixel–text matching into the prompt encoder for SAM, effectively leveraging textual semantic consistency while preserving SAM’s extensive segmentation knowledge. By incorporating task-specific structures such as an attention mask, our model achieves superior few-shot segmentation performance with only 1.4 M learnable parameters. Evaluations on PASCAL-5ⁱ and COCO-20ⁱ datasets demonstrate that VLP-SAM significantly outperforms previous methods by 6.8% and 9.3% in mIoU, respectively. Furthermore, VLP-SAM exhibits strong generalization across unseen objects and cross-domain scenarios, highlighting the robustness provided by textual semantic guidance. This study offers an effective and scalable framework for few-shot segmentation with multimodal prompts. Full article

(This article belongs to the Special Issue Trustworthy Multimodal Vision Models: Generalization, Robustness, and Explainability)

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20 pages, 6234 KB

Open AccessArticle

Wafer Defect Recognition for Industrial Inspection: FCS-VMamba Model and Experimental Validation

by Yijia Zhang, Ziyi Ma, Tongji Cui, Tiejun Zhao, Qi Wang and Jianhua Wang

J. Imaging 2026, 12(4), 142; https://doi.org/10.3390/jimaging12040142 - 24 Mar 2026

Abstract

In industrial imaging scenarios, semiconductor wafer defect classification is crucial for chip manufacturing yield and reliability. However, numerous challenges persist, including weak imaging responses and detail loss during downsampling, complex backgrounds that interfere with feature extraction, and the trade-off between performance and efficiency [...] Read more.

In industrial imaging scenarios, semiconductor wafer defect classification is crucial for chip manufacturing yield and reliability. However, numerous challenges persist, including weak imaging responses and detail loss during downsampling, complex backgrounds that interfere with feature extraction, and the trade-off between performance and efficiency on edge devices. Traditional CNNs and ViTs exhibit limitations in modeling long-range dependencies and managing edge deployment costs. To address these issues, we leverage the VMamba architecture, a Visual State Space Model (SSM) that achieves global contextual modeling with linear computational complexity. Based on the VMamba architecture, we propose FCS-VMamba, a domain-adapted model that integrates three core modules, namely Frequency Attention (FA), Cross-Layer Cross-Attention (CLCA), and Saliency Feature Suppression (SFS). The experimental results show that FCS-VMamba achieved

86.06 %

macro-precision and

87.91 %

Top-1 accuracy with only

1.2

M parameters. These results demonstrate that FCS-VMamba provides a practical and parameter-efficient baseline for industrial wafer defect recognition. Full article

(This article belongs to the Section AI in Imaging)

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18 pages, 6071 KB

Open AccessArticle

DFENet: A Novel Dual-Path Feature Extraction Network for Semantic Segmentation of Remote Sensing Images

by Li Cao, Zishang Liu, Yan Wang and Run Gao

J. Imaging 2026, 12(3), 141; https://doi.org/10.3390/jimaging12030141 - 23 Mar 2026

Abstract

Semantic segmentation of remote sensing images (RSIs) is a fundamental task in geoscience research. However, designing efficient feature fusion modules remains challenging for existing dual-branch or multi-branch architectures. Furthermore, existing deep learning-based architectures predominantly concentrate on spatial feature modeling and context capturing while [...] Read more.

Semantic segmentation of remote sensing images (RSIs) is a fundamental task in geoscience research. However, designing efficient feature fusion modules remains challenging for existing dual-branch or multi-branch architectures. Furthermore, existing deep learning-based architectures predominantly concentrate on spatial feature modeling and context capturing while inherently neglecting the exploration and utilization of critical frequency-domain features, which is crucial for addressing issues of semantic confusion and blurred boundaries in complex remote sensing scenes. To address the challenges of feature fusion and the lack of frequency-domain information, we propose a novel dual-path feature extraction network (DFENet) in this paper. Specifically, a dual-path module (DPM) is developed in DFENet to extract global and local features, respectively. In the global path, after applying the channel splitting strategy, four feature extraction strategies are innovatively integrated to extract global features from different granularities. According to the strategy of supplementing frequency-domain information, a frequency-domain feature extraction block (FFEB) dominated by discrete Wavelet transform (DWT) is designed to effectively captures both high- and low-frequency components. Experimental results show that our method outperforms existing state-of-the-art methods in terms of segmentation performance, achieving a mean intersection over union (mIoU) of 83.09% on the ISPRS Vaihingen dataset and 86.05% on the ISPRS Potsdam dataset. Full article

(This article belongs to the Section Image and Video Processing)

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21 pages, 4335 KB

Open AccessArticle

Real-Time Small UAV Detection in Complex Airspace Using YOLOv11 with Residual Attention and High-Resolution Feature Enhancement

by Chuang Han, Md Redwan Ullah, Amrul Kayes, Khalid Hasan, Md Abdur Rouf, Md Rakib Hasan, Shen Tao, Guo Gengli and Mohammad Masum Billah

J. Imaging 2026, 12(3), 140; https://doi.org/10.3390/jimaging12030140 - 20 Mar 2026

Abstract

Detecting small unmanned aerial vehicles (UAVs) in complex airspace presents significant challenges due to their minimal pixel footprint, resemblance to birds, and frequent occlusion. To address these issues, we propose YOLOv11-ResCBAM, a novel real-time detection framework that integrates a Residual Convolutional Block Attention [...] Read more.

Detecting small unmanned aerial vehicles (UAVs) in complex airspace presents significant challenges due to their minimal pixel footprint, resemblance to birds, and frequent occlusion. To address these issues, we propose YOLOv11-ResCBAM, a novel real-time detection framework that integrates a Residual Convolutional Block Attention Module (ResCBAM) and a high-resolution P2 detection head into the YOLOv11 architecture. ResCBAM enhances channel and spatial feature refinement while preserving original feature contexts through residual connections, and the P2 head maintains fine spatial details crucial for small-object localization. Evaluated on a custom dataset of 4917 images (11,733 after augmentation) across three classes (drone, bird, airplane), our model achieves a mean average precision at the 0.5–0.95 IoU threshold (mAP@0.5–0.95) of 0.845, representing a 7.9% improvement over the baseline YOLOv11n, while maintaining real-time inference at 50.51 FPS. Cross-dataset validation on VisDrone2019-DET and UAVDT benchmarks demonstrates promising generalization trends. This work demonstrates the effectiveness of the proposed approach for UAV surveillance systems, balancing detection accuracy with computational efficiency for deployment in security-critical environments. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 3rd Edition)

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29 pages, 5347 KB

Open AccessArticle

Optimized Reinforcement Learning-Driven Model for Remote Sensing Change Detection

by Yan Zhao, Zhiyun Xiao, Tengfei Bao and Yulong Zhou

J. Imaging 2026, 12(3), 139; https://doi.org/10.3390/jimaging12030139 - 19 Mar 2026

Abstract

In recent years, deep learning has driven remarkable progress in remote sensing change detection (CD); however, practical deployment is still hindered by two limitations. First, CD results are easily degraded by imaging-induced uncertainties—mixed pixels and blurred boundaries, radiometric inconsistencies (e.g., shadows and seasonal [...] Read more.

In recent years, deep learning has driven remarkable progress in remote sensing change detection (CD); however, practical deployment is still hindered by two limitations. First, CD results are easily degraded by imaging-induced uncertainties—mixed pixels and blurred boundaries, radiometric inconsistencies (e.g., shadows and seasonal illumination changes), and slight residual misregistration—leading to pseudo-changes and fragmented boundaries. Second, prevailing methods follow a static one-pass inference paradigm and lack an explicit feedback mechanism for adaptive error correction, which weakens generalization in complex or unseen scenes. To address these issues, we propose a feedback-driven CD framework that integrates a dual-branch U-Net with deep reinforcement learning (RL) for pixel-level probabilistic iterative refinement of an initial change probability map. The backbone produces a preliminary posterior estimate of change likelihood from multi-scale bi-temporal features, while a PPO-based RL agent formulates refinement as a Markov decision process. The agent leverages a state representation that fuses multi-scale features, prediction confidence/uncertainty, and spatial consistency cues (e.g., neighborhood coherence and edge responses) to apply multi-step corrective actions. From an imaging and interpretation perspective, the RL module can be viewed as a learnable, self-adaptive imaging optimization mechanism: for high-risk regions affected by blurred boundaries, radiometric inconsistencies, and local misalignment, the agent performs feedback-driven multi-step corrections to improve boundary fidelity and spatial coherence while suppressing pseudo-changes caused by shadows and illumination variations. Experiments on four datasets (CDD, SYSU-CD, PVCD, and BRIGHT) verify consistent improvements. Using SiamU-Net as an example, the proposed RL refinement increases mIoU by 3.07, 2.54, 6.13, and 3.1 points on CDD, SYSU-CD, PVCD, and BRIGHT, respectively, with similarly consistent gains observed when the same RL module is integrated into other representative CD backbones. Full article

(This article belongs to the Section AI in Imaging)

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15 pages, 2099 KB

Open AccessReview

Current Trends and Future Prospects of Radiomics and Machine Learning (ML) Models in Spinal Tumors—A Narrative Review

by Vivek Sanker, Suhrud Panchawgh, Anmol Kaur, Vinay Suresh, Dhanya Mahesh, Eeman Ahmad, Srinath Hariharan, Dhiraj Pangal, Maria Jose Cavgnaro, Mirabela Rusu, John Ratliff and Atman Desai

J. Imaging 2026, 12(3), 138; https://doi.org/10.3390/jimaging12030138 - 19 Mar 2026

Abstract

The intersection between radiomics, the computational analysis of imaging data, and machine learning (ML) may lead to new developments in the diagnosis, prognosis, and management of diseases. For spinal tumors specifically, applications of these fields appear promising. In this educational narrative review, we [...] Read more.

The intersection between radiomics, the computational analysis of imaging data, and machine learning (ML) may lead to new developments in the diagnosis, prognosis, and management of diseases. For spinal tumors specifically, applications of these fields appear promising. In this educational narrative review, we provide a summary of the current advancements in radiomics and artificial intelligence (AI), as well as applications of both fields in the diagnosis and management of spinal tumors. We also provide a suggested workflow of radiomics and machine learning analysis of spinal tumors for researchers, including a list and description of commonly used radiomic features. Future directions in the field of radiomics and machine learning applications to spinal tumors may involve validating already proposed algorithms with larger datasets, ensuring that all computational applications to patient care maintain high ethical standards, and continuing work in developing novel and highly accurate computational techniques to enhance patient outcomes. Full article

(This article belongs to the Topic Machine Learning and Deep Learning in Medical Imaging)

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