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Celebrating the 10th Anniversary of the Journal of Imaging

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A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 10264

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Special Issue Editors

Prof. Dr. Raimondo Schettini

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Guest Editor

Department of Informatics, Systems and Communication, University of Milano-Bicocca, viale Sarca, 336, 20126 Milano, Italy
Interests: color imaging; image and video processing; analysis and classification; visual information systems; image quality
Special Issues, Collections and Topics in MDPI journals

Dr. Guanghui (Richard) Wang

E-Mail Website
Guest Editor

Department of Computer Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
Interests: computer vision; image processing; machine learning; intelligent systems

Special Issue Information

Dear Colleagues,

The MDPI Journal of Imaging is delighted to announce a Special Issue to commemorate its 10th anniversary. Over the past decade, the journal has been at the forefront of publishing high-quality research across all areas of imaging science and technology. This milestone reflects the invaluable contributions of our authors, reviewers, and readers worldwide.

To mark this occasion, we are inviting submissions for a Special Issue that will showcase the most innovative, impactful, and visionary research in imaging science. We welcome contributions from both well-established experts and emerging researchers, aiming to provide a comprehensive view of the current state and future directions of imaging. Submissions are encouraged in the following areas:

- Image Processing and Analysis: New methodologies, algorithms, and applications.
- Computer Vision: Advances in object detection, recognition, and scene understanding.
- Multimodal Imaging: Integration and analysis of data from different imaging modalities.
- Medical Imaging: Novel techniques for diagnosis, treatment, and monitoring.
- Remote Sensing and Satellite Imaging: Applications in environmental monitoring and earth observation.
- Image Quality and Enhancement: Perception-driven quality metrics and enhancement techniques.
- Imaging Systems and Devices: Advances in hardware and software systems.
- Emerging Applications: Imaging in art, archeology, cultural heritage, and beyond.
- Artificial Intelligence in Imaging: Deep learning and other AI techniques applied to imaging tasks.
- Ethics and Sustainability in Imaging: Addressing challenges related to fairness, privacy, and energy efficiency.

We invite you to join us in celebrating this important milestone by contributing to a special publication that highlights the journal's legacy and achievements. This is a unique opportunity to share your groundbreaking research with a global audience, make an impact in the field of imaging science, and play a significant role in shaping the future of this ever-evolving discipline.

All submissions will undergo a rigorous peer-review process. Authors are encouraged to follow the Journal of Imaging author guidelines (https://www.mdpi.com/journal/jimaging/instructions) when preparing their manuscripts.

We look forward to receiving your contributions and celebrating this significant milestone together!

Prof. Dr. Raimondo Schettini
Dr. Guanghui (Richard) Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly 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 1800 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
machine learning
object detection
multimodal Imaging
medical imaging
remote sensing
image quality
imaging systems and devices
artificial intelligence

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Published Papers (9 papers)

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Research

26 pages, 6612 KB

Open AccessArticle

A Comparative Survey of Vision Transformers for Feature Extraction in Texture Analysis

by Leonardo Scabini, Andre Sacilotti, Kallil M. Zielinski, Lucas C. Ribas, Bernard De Baets and Odemir M. Bruno

J. Imaging 2025, 11(9), 304; https://doi.org/10.3390/jimaging11090304 - 5 Sep 2025

Viewed by 601

Abstract

Texture, a significant visual attribute in images, plays an important role in many pattern recognition tasks. While Convolutional Neural Networks (CNNs) have been among the most effective methods for texture analysis, alternative architectures such as Vision Transformers (ViTs) have recently demonstrated superior performance on a range of visual recognition problems. However, the suitability of ViTs for texture recognition remains underexplored. In this work, we investigate the capabilities and limitations of ViTs for texture recognition by analyzing 25 different ViT variants as feature extractors and comparing them to CNN-based and hand-engineered approaches. Our evaluation encompasses both accuracy and efficiency, aiming to assess the trade-offs involved in applying ViTs to texture analysis. Our results indicate that ViTs generally outperform CNN-based and hand-engineered models, particularly when using strong pre-training and in-the-wild texture datasets. Notably, BeiTv2-B/16 achieves the highest average accuracy (85.7%), followed by ViT-B/16-DINO (84.1%) and Swin-B (80.8%), outperforming the ResNet50 baseline (75.5%) and the hand-engineered baseline (73.4%). As a lightweight alternative, EfficientFormer-L3 attains a competitive average accuracy of 78.9%. In terms of efficiency, although ViT-B and BeiT(v2) have a higher number of GFLOPs and parameters, they achieve significantly faster feature extraction on GPUs compared to ResNet50. These findings highlight the potential of ViTs as a powerful tool for texture analysis while also pointing to areas for future exploration, such as efficiency improvements and domain-specific adaptations. Full article

(This article belongs to the Special Issue Celebrating the 10th Anniversary of the Journal of Imaging)

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18 pages, 16540 KB

Open AccessArticle

E-CMCA and LSTM-Enhanced Framework for Cross-Modal MRI-TRUS Registration in Prostate Cancer

by Ciliang Shao, Ruijin Xue and Lixu Gu

J. Imaging 2025, 11(9), 292; https://doi.org/10.3390/jimaging11090292 - 27 Aug 2025

Viewed by 414

Abstract

Accurate registration of MRI and TRUS images is crucial for effective prostate cancer diagnosis and biopsy guidance, yet modality differences and non-rigid deformations pose significant challenges, especially in dynamic imaging. This study presents a novel cross-modal MRI-TRUS registration framework, leveraging a dual-encoder architecture with an Enhanced Cross-Modal Channel Attention (E-CMCA) module and a LSTM-Based Spatial Deformation Modeling Module. The E-CMCA module efficiently extracts and integrates multi-scale cross-modal features, while the LSTM-Based Spatial Deformation Modeling Module models temporal dynamics by processing depth-sliced 3D deformation fields as sequential data. A VecInt operation ensures smooth, diffeomorphic transformations, and a FuseConv layer enhances feature integration for precise alignment. Experiments on the

μ

-RegPro dataset from the MICCAI 2023 Challenge demonstrate that our model significantly improves registration accuracy and performs robustly in both static 3D and dynamic 4D registration tasks. Experiments on the

μ

-RegPro dataset from the MICCAI 2023 Challenge demonstrate that our model achieves a DSC of 0.865, RDSC of 0.898, TRE of 2.278 mm, and RTRE of 1.293, surpassing state-of-the-art methods and performing robustly in both static 3D and dynamic 4D registration tasks. Full article

(This article belongs to the Special Issue Celebrating the 10th Anniversary of the Journal of Imaging)

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47 pages, 18189 KB

Open AccessArticle

Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures

by Zineb Sordo, Eric Chagnon, Zixi Hu, Jeffrey J. Donatelli, Peter Andeer, Peter S. Nico, Trent Northen and Daniela Ushizima

J. Imaging 2025, 11(8), 252; https://doi.org/10.3390/jimaging11080252 - 26 Jul 2025

Viewed by 2560

Abstract

Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model’s foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research. Full article

(This article belongs to the Special Issue Celebrating the 10th Anniversary of the Journal of Imaging)

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17 pages, 1788 KB

Open AccessArticle

Detection of Double Compression in HEVC Videos Containing B-Frames

by Yoshihisa Furushita, Daniele Baracchi, Marco Fontani, Dasara Shullani and Alessandro Piva

J. Imaging 2025, 11(7), 211; https://doi.org/10.3390/jimaging11070211 - 27 Jun 2025

Viewed by 617

Abstract

This study proposes a method to detect double compression in H.265/HEVC videos containing B-frames, a scenario underexplored in previous research. The method extracts frame-level encoding features—including frame type, coding unit (CU) size, quantization parameter (QP), and prediction modes—and represents each video as a 28-dimensional feature vector. A bidirectional Long Short-Term Memory (Bi-LSTM) classifier is then trained to model temporal inconsistencies introduced during recompression. To evaluate the method, we created a dataset of 129 HEVC-encoded YUV videos derived from 43 original sequences, covering various bitrate combinations and GOP structures. The proposed method achieved a detection accuracy of 80.06%, outperforming two existing baselines. These results demonstrate the practical applicability of the proposed approach in realistic double compression scenarios. Full article

(This article belongs to the Special Issue Celebrating the 10th Anniversary of the Journal of Imaging)

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30 pages, 25530 KB

Open AccessArticle

Towards the Performance Characterization of a Robotic Multimodal Diagnostic Imaging System

by George Papaioannou, Christos Mitrogiannis, Mark Schweitzer, Nikolaos Michailidis, Maria Pappa, Pegah Khosravi, Apostolos Karantanas, Sean Starling and Christian Ruberg

J. Imaging 2025, 11(5), 147; https://doi.org/10.3390/jimaging11050147 - 7 May 2025

Viewed by 1118

Abstract

Characterizing imaging performance requires a multidisciplinary approach that evaluates various interconnected parameters, including dosage optimization and dynamic accuracy. Radiation dose and dynamic accuracy are challenged by patient motion that results in poor image quality. These challenges are more prevalent in the brain/cardiac pediatric patient imaging, as they relate to excess radiation dose that may be associated with various complications. Scanning vulnerable pediatric patients ought to eliminate anesthesia due to critical risks associated in some cases with intracranial hemorrhages, brain strokes, and congenital heart disease. Some pediatric imaging, however, requires prolonged scanning under anesthesia. It can often be a laborious, suboptimal process, with limited field of view and considerable dose. High dynamic accuracy is also necessary to diagnose tissue’s dynamic behavior beyond its static structural morphology. This study presents several performance characterization experiments from a new robotic multimodal imaging system using specially designed calibration methods at different system configurations. Additional musculoskeletal imaging and imaging from a pediatric brain stroke patient without anesthesia are presented for comparisons. The findings suggest that the system’s large dynamically controlled gantry enables scanning at full patient movement and with important improvements in scan times, accuracy, radiation dose, and the ability to image brain structures without anesthesia. This could position the system as a potential transformative tool in the pediatric interventional imaging landscape. Full article

(This article belongs to the Special Issue Celebrating the 10th Anniversary of the Journal of Imaging)

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17 pages, 2046 KB

Open AccessArticle

Breast Lesion Detection Using Weakly Dependent Customized Features and Machine Learning Models with Explainable Artificial Intelligence

by Simona Moldovanu, Dan Munteanu, Keka C. Biswas and Luminita Moraru

J. Imaging 2025, 11(5), 135; https://doi.org/10.3390/jimaging11050135 - 28 Apr 2025

Viewed by 789

Abstract

This research proposes a novel strategy for accurate breast lesion classification that combines explainable artificial intelligence (XAI), machine learning (ML) classifiers, and customized weakly dependent features from ultrasound (BU) images. Two new weakly dependent feature classes are proposed to improve the diagnostic accuracy and diversify the training data. These are based on image intensity variations and the area of bounded partitions and provide complementary rather than overlapping information. ML classifiers such as Random Forest (RF), Extreme Gradient Boosting (XGB), Gradient Boosting Classifiers (GBC), and LASSO regression were trained with both customized feature classes. To validate the reliability of our study and the results obtained, we conducted a statistical analysis using the McNemar test. Later, an XAI model was combined with ML to tackle the influence of certain features, the constraints of feature selection, and the interpretability capabilities across various ML models. LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) models were used in the XAI process to enhance the transparency and interpretation in clinical decision-making. The results revealed common relevant features for the malignant class, consistently identified by all of the classifiers, and for the benign class. However, we observed variations in the feature importance rankings across the different classifiers. Furthermore, our study demonstrates that the correlation between dependent features does not impact explainability. Full article

(This article belongs to the Special Issue Celebrating the 10th Anniversary of the Journal of Imaging)

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23 pages, 37586 KB

Open AccessArticle

Revisiting Wölfflin in the Age of AI: A Study of Classical and Baroque Composition in Generative Models

by Adrien Deliege, Maria Giulia Dondero and Enzo D’Armenio

J. Imaging 2025, 11(5), 128; https://doi.org/10.3390/jimaging11050128 - 22 Apr 2025

Cited by 1 | Viewed by 890

Abstract

This study explores how contemporary text-to-image models interpret and generate Classical and Baroque styles under Wölfflin’s framework—two categories that are atemporal and transversal across media. Our goal is to see whether generative AI can replicate the nuanced stylistic cues that art historians attribute to them. We prompted two popular models (DALL•E and Midjourney) using explicit style labels (e.g., “baroque” and “classical”) as well as more implicit cues (e.g., “dynamic”, “static”, or reworked Wölfflin descriptors). We then collected expert ratings and conducted broader qualitative reviews to assess how each output aligned with Wölfflin’s characteristics. Our findings suggest that the term “baroque” usually evokes features recognizable in typically historical Baroque artworks, while “classical” often yields less distinct results, particularly when a specified genre (portrait, still life) imposes a centered, closed-form composition. Removing explicit style labels may produce highly abstract images, revealing that Wölfflin’s descriptors alone may be insufficient to convey Classical or Baroque styles efficiently. Interestingly, the term “dynamic” gives rather chaotic images, yet this chaos is somehow ordered, centered, and has an almost Classical feel. Altogether, these observations highlight the complexity of bridging canonical stylistic frameworks and contemporary AI training biases, underscoring the need to update or refine Wölfflin’s atemporal categories to accommodate how generative models—and modern visual culture—reinterpret Classical and Baroque. Full article

(This article belongs to the Special Issue Celebrating the 10th Anniversary of the Journal of Imaging)

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24 pages, 11715 KB

Open AccessArticle

Assessing Cancer Presence in Prostate MRI Using Multi-Encoder Cross-Attention Networks

by Avtantil Dimitriadis, Grigorios Kalliatakis, Richard Osuala, Dimitri Kessler, Simone Mazzetti, Daniele Regge, Oliver Diaz, Karim Lekadir, Dimitrios Fotiadis, Manolis Tsiknakis, Nikolaos Papanikolaou, ProCAncer-I Consortium and Kostas Marias

J. Imaging 2025, 11(4), 98; https://doi.org/10.3390/jimaging11040098 - 26 Mar 2025

Viewed by 1020

Abstract

Prostate cancer (PCa) is currently the second most prevalent cancer among men. Accurate diagnosis of PCa can provide effective treatment for patients and reduce mortality. Previous works have merely focused on either lesion detection or lesion classification of PCa from magnetic resonance imaging (MRI). In this work we focus on a critical, yet underexplored task of the PCa clinical workflow: distinguishing cases with cancer presence (pathologically confirmed PCa patients) from conditions with no suspicious PCa findings (no cancer presence). To this end, we conduct large-scale experiments for this task for the first time by adopting and processing the multi-centric ProstateNET Imaging Archive which contains more than 6 million image representations of PCa from more than 11,000 PCa cases, representing the largest collection of PCa MR images. Bi-parametric MR (bpMRI) images of 4504 patients alongside their clinical variables are used for training, while the architectures are evaluated on two hold-out test sets of 975 retrospective and 435 prospective patients. Our proposed multi-encoder-cross-attention-fusion architecture achieved a promising area under the receiver operating characteristic curve (AUC) of 0.91. This demonstrates our method’s capability of fusing complex bi-parametric imaging modalities and enhancing model robustness, paving the way towards the clinical adoption of deep learning models for accurately determining the presence of PCa across patient populations. Full article

(This article belongs to the Special Issue Celebrating the 10th Anniversary of the Journal of Imaging)

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21 pages, 4293 KB

Open AccessArticle

A Highly Robust Encoder–Decoder Network with Multi-Scale Feature Enhancement and Attention Gate for the Reduction of Mixed Gaussian and Salt-and-Pepper Noise in Digital Images

by Milan Tripathi, Waree Kongprawechnon and Toshiaki Kondo

J. Imaging 2025, 11(2), 51; https://doi.org/10.3390/jimaging11020051 - 10 Feb 2025

Cited by 1 | Viewed by 1284

Abstract

Image denoising is crucial for correcting distortions caused by environmental factors and technical limitations. We propose a novel and highly robust encoder–decoder network (HREDN) for effectively removing mixed salt-and-pepper and Gaussian noise from digital images. HREDN integrates a multi-scale feature enhancement block in the encoder, allowing the network to capture features at various scales and handle complex noise patterns more effectively. To mitigate information loss during encoding, skip connections transfer essential feature maps from the encoder to the decoder, preserving structural details. However, skip connections can also propagate redundant information. To address this, we incorporate attention gates within the skip connections, ensuring that only relevant features are passed to the decoding layers. We evaluate the robustness of the proposed method across facial, medical, and remote sensing domains. The experimental results demonstrate that HREDN excels in preserving edge details and structural features in denoised images, outperforming state-of-the-art techniques in both qualitative and quantitative measures. Statistical analysis further highlights the model’s ability to effectively remove noise in diverse, complex scenarios with images of varying resolutions across multiple domains. Full article

(This article belongs to the Special Issue Celebrating the 10th Anniversary of the Journal of Imaging)

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