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A Novel Method to Compute the Contact Surface Area Between an Organ and Cancer Tissue
Sowing, Monitoring, Detecting: A Possible Solution to Improve the Visibility of Cropmarks in Cultivated Fields
A Method for Estimating Fluorescence Emission Spectra from the Image Data of Plant Grain and Leaves Without a Spectrometer
SAVE: Self-Attention on Visual Embedding for Zero-Shot Generic Object Counting

Journal Description

Journal of Imaging

Journal of Imaging is an international, multi/interdisciplinary, peer-reviewed, open access journal of imaging techniques published online monthly by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), PubMed, PMC, dblp, Inspec, Ei Compendex, and other databases.
Journal Rank: CiteScore - Q1 (Computer Graphics and Computer-Aided Design)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.3 days after submission; acceptance to publication is undertaken in 3.3 days (median values for papers published in this journal in the second half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 2.7 (2023); 5-Year Impact Factor: 3.0 (2023)

Imprint Information Journal Flyer Open Access ISSN: 2313-433X

Latest Articles

24 pages, 4056 KiB

Open AccessArticle

Unveiling the Ultimate Meme Recipe: Image Embeddings for Identifying Top Meme Templates from r/Memes

by Jan Sawicki

J. Imaging 2025, 11(5), 132; https://doi.org/10.3390/jimaging11050132 - 23 Apr 2025

Meme analysis, particularly identifying top meme templates, is crucial for understanding digital culture, communication trends, and the spread of online humor, as memes serve as units of cultural transmission that shape public discourse. Tracking popular templates enables researchers to examine their role in [...] Read more.

Meme analysis, particularly identifying top meme templates, is crucial for understanding digital culture, communication trends, and the spread of online humor, as memes serve as units of cultural transmission that shape public discourse. Tracking popular templates enables researchers to examine their role in social engagement, ideological framing, and viral dynamics within digital ecosystems. This study explored the viral nature of memes by analyzing a large dataset of over 1.5 million meme submissions from Reddit’s r/memes subreddit, spanning from January 2021 to July 2024. The focus was on uncovering the most popular meme templates by applying advanced image processing techniques. Apart from building an overall understanding of the memesphere, the main contribution was a selection of top meme templates providing a recipe for the best meme template for the meme creators (memesters). Using Vision Transformer (ViT) models, visual features of memes were analyzed without the influence of text, and memes were grouped into 1000 clusters that represented distinct templates. By combining image captioning and keyword extraction methods, key characteristics of the templates were identified, highlighting those with the most visual consistency. A deeper examination of the most popular memes revealed that factors like timing, cultural relevance, and references to current events played a significant role in their virality. Although user identity had limited influence on meme success, a closer look at contributors revealed an interesting pattern of a bot account and two prominent users. Ultimately, the study pinpointed the ten most popular meme templates, many of which were based on pop culture, offering insights into what makes a meme likely to go viral in today’s digital culture. Full article

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

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11 pages, 1644 KiB

Open AccessCommunication

Improvements in Image Registration, Segmentation, and Artifact Removal in ThermOcular Imaging System

by Navid Shahsavari, Ehsan Zare Bidaki, Alexander Wong and Paul J. Murphy

J. Imaging 2025, 11(5), 131; https://doi.org/10.3390/jimaging11050131 - 23 Apr 2025

The assessment of ocular surface temperature (OST) plays a pivotal role in the diagnosis and management of various ocular diseases. This paper introduces significant enhancements to the ThermOcular system, initially developed for precise OST measurement using infrared (IR) thermography. These advancements focus on [...] Read more.

The assessment of ocular surface temperature (OST) plays a pivotal role in the diagnosis and management of various ocular diseases. This paper introduces significant enhancements to the ThermOcular system, initially developed for precise OST measurement using infrared (IR) thermography. These advancements focus on accuracy improvements that reduce user dependency and increase the system’s diagnostic capabilities. A novel addition to the system includes the use of EyeTags, which assist clinicians in selecting control points more easily, thus reducing errors associated with manual selection. Furthermore, the integration of state-of-the-art semantic segmentation models trained on the newest dataset is explored. Among these, the OCRNet-HRNet-w18 model achieved a segmentation accuracy of 96.21% MIOU, highlighting the effectiveness of the improved pipeline. Additionally, the challenge of eliminating eyelashes in IR frames, which cause artifactual measurement errors in OST assessments, is addressed. Through a newly developed method, the influence of eyelashes is eliminated, thereby enhancing the precision of temperature readings. Moreover, an algorithm for blink detection and elimination is implemented, significantly improving upon the basic methods previously utilized. These innovations not only enhance the reliability of OST measurements, but also contribute to the system’s efficiency and diagnostic accuracy, marking a significant step forward in ocular health monitoring and diagnostics. Full article

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

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13 pages, 406 KiB

Open AccessArticle

Correlation Between SCORE2-Diabetes and Coronary Artery Calcium Score in Patients with Type 2 Diabetes Mellitus: A Cross-Sectional Study in Vietnam

by Hung Phi Truong, Hoang Minh Tran, Thuan Huynh, Dung N. Q. Nguyen, Dung Thuong Ho, Cuong Cao Tran, Sang Van Nguyen and Tuan Minh Vo

J. Imaging 2025, 11(5), 130; https://doi.org/10.3390/jimaging11050130 - 23 Apr 2025

(1) Background: The SCORE2-Diabetes model has been developed as an effective tool to estimate the 10-year cardiovascular risk in patients with diabetes. Coronary computed tomography angiography (CCTA) and its derived Coronary Artery Calcium Score (CACS) are widely used non-invasive imaging tools for assessing [...] Read more.

(1) Background: The SCORE2-Diabetes model has been developed as an effective tool to estimate the 10-year cardiovascular risk in patients with diabetes. Coronary computed tomography angiography (CCTA) and its derived Coronary Artery Calcium Score (CACS) are widely used non-invasive imaging tools for assessing coronary artery disease (CAD). This study aimed to evaluate the correlation between CACS and SCORE2-Diabetes in patients with T2DM. (2) Methods: A cross-sectional study was conducted from October 2023 to May 2024. We included patients aged 40 to 69 years with T2DM who underwent a coronary multislice CT scan due to atypical angina. The correlation between CACS and SCORE2-Diabetes was analyzed using Spearman’s rank correlation coefficient. (3) Results: A total of 100 patients met the inclusion criteria, including 71 males and 29 females, with a mean age of 61.9 ± 5.4 years. The differences in CACS and SCORE2-Diabetes among different degrees of coronary artery stenosis were statistically significant (p < 0.05). A statistically significant but weak positive correlation was observed between CACS and SCORE2-Diabetes across all risk categories, with Spearman’s rank correlation coefficients ranging from 0.27 to 0.28 (p < 0.01). (4) Conclusions: Despite the weak correlation between CACS and SCORE2-Diabetes, understanding their relationship and independent associations with disease severity is valuable. The combination of these two tools may warrant investigation in future studies to potentially enhance cardiovascular risk assessment in T2DM patients. Full article

(This article belongs to the Section Medical Imaging)

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26 pages, 13259 KiB

Open AccessArticle

Method for Indoor Seismic Intensity Assessment Based on Image Processing Techniques

by Jingsong Yang, Guoxing Lu, Yanxiong Wu and Fumin Peng

J. Imaging 2025, 11(5), 129; https://doi.org/10.3390/jimaging11050129 - 22 Apr 2025

The seismic intensity experienced indoors directly reflects the degree of damage to the internal structure of the building. The current classification of indoor strength relies on manual surveys and qualitative descriptions of macro phenomena, which are subjective, unable to capture real-time dynamic changes [...] Read more.

The seismic intensity experienced indoors directly reflects the degree of damage to the internal structure of the building. The current classification of indoor strength relies on manual surveys and qualitative descriptions of macro phenomena, which are subjective, unable to capture real-time dynamic changes in the room, and lack quantitative indicators. In this paper, we present the Image Evaluation of Seismic Intensity (IESI) method, which is based on image processing technology. This method mainly evaluates the degree of responses from objects by identifying the percentage of movement of different types of objects in images taken before and after an earthquake. In order to further improve the recognition accuracy, we combined the camera vibration degree and the object displacement between images to correct the generated earthquake intensity level estimation, so as to achieve the rapid assessment of an earthquake’s intensity indoors. We took, as an example, 29 sets of seismic data from different scenarios. We used the IESI method to evaluate the seismic intensity of these scenarios. Compared with the seismic intensity evaluation results obtained by the Post-disaster Sensor-based Condition Assessment of Buildings (PSAB) and the Image-based Seismic Damage Assessment System (IDEAS) methods, the accuracy of the IESI method was higher by more than 30%, and its accuracy reached 97%. The universality of the IESI method in different indoor scenarios was demonstrated. In a low-intensity evaluation experiment, the accuracy of the IESI method also reached 91%, which verifies the reliability of the IESI method in low-intensity regions. Full article

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

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23 pages, 37586 KiB

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

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 [...] Read more.

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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10 pages, 928 KiB

Open AccessArticle

Reliability of Quadriceps and Hamstring Soft Tissue Measures Using Dual-Energy X-Ray Absorptiometry (DXA) Scans

by Trey R. Naylor, Mariana V. Jacobs, Cameron J. Elder, Michael A. Samaan and Jody L. Clasey

J. Imaging 2025, 11(5), 127; https://doi.org/10.3390/jimaging11050127 - 22 Apr 2025

Purpose: The purpose of this study was to determine the intra- and interrater reliability of quadriceps and hamstring soft tissue measures using DXA scans. Methods: A total of 44 subjects (23 males) participated in this study. The first total body DXA scan was [...] Read more.

Purpose: The purpose of this study was to determine the intra- and interrater reliability of quadriceps and hamstring soft tissue measures using DXA scans. Methods: A total of 44 subjects (23 males) participated in this study. The first total body DXA scan was performed in the standard anterior/posterior scanning position, followed by two additional total body scans while the subjects were lying on their left and right sides with the leg of interest extended and the contralateral leg bent. Unique regions of interest were created for analyses of mineral-free lean masses (MFL) using custom analysis software with manual tracing (by two investigators) of the quadriceps (QUADS) and hamstrings (HAMS) of the right and left thighs. Between–within repeated measure analysis of variance (ANOVA) was used to determine if there were significant differences among the MFL measures, while intraclass correlation coefficients (ICC) and coefficients of variation (CV) were used to assess the intra- and interrater reliability. Results: Between-group analyses revealed that Investigator 2 had small yet significantly higher mean differences for right QUADS (2346.6 ± 602.4 g vs. 2327.4 ± 587.9 g), left QUADS (2337.3 ± 581.9 g vs. 2312.8 ± 581.2 g), right HAMS (2655.9 ± 626.3 g vs. 2543.0 ± 593.5 g), and left HAMS (2686.1 ± 628.1 g vs. 2562.8 ± 596.5 g) when compared to Investigator 1. Intraclass correlation coefficients between (≥0.984) and within (≥0.992) raters were high for all MFL measures, with low variation across all MFL measures (≤1.62%). Conclusions: Despite having significant group mean differences, our results revealed strong and significant reliability, and we recommend that a single investigator analyze the scans twice and that the mean of the two measures be used for final reporting within a given study. Full article

(This article belongs to the Section Medical Imaging)

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17 pages, 39878 KiB

Open AccessArticle

Real-Time Volume-Rendering Image Denoising Based on Spatiotemporal Weighted Kernel Prediction

by Xinran Xu, Chunxiao Xu and Lingxiao Zhao

J. Imaging 2025, 11(4), 126; https://doi.org/10.3390/jimaging11040126 - 21 Apr 2025

Volumetric Path Tracing (VPT) based on Monte Carlo (MC) sampling often requires numerous samples for high-quality images, but real-time applications limit samples to maintain interaction rates, leading to significant noise. Traditional real-time denoising methods use radiance and geometric features as neural network inputs, [...] Read more.

Volumetric Path Tracing (VPT) based on Monte Carlo (MC) sampling often requires numerous samples for high-quality images, but real-time applications limit samples to maintain interaction rates, leading to significant noise. Traditional real-time denoising methods use radiance and geometric features as neural network inputs, but lightweight networks struggle with temporal stability and complex mapping relationships, causing blurry results. To address these issues, a spatiotemporal lightweight neural network is proposed to enhance the denoising performance of VPT-rendered images with low samples per pixel. First, the reprojection technique was employed to obtain features from historical frames. Next, a dual-input convolutional neural network architecture was designed to predict filtering kernels. Radiance and geometric features were encoded independently. The encoding of geometric features guided the pixel-wise fitting of radiance feature filters. Finally, learned weight filtering kernels were applied to images’ spatiotemporal filtering to produce denoised results. The experimental results across multiple denoising datasets demonstrate that this approach outperformed the baseline models in terms of feature extraction and detail representation capabilities while effectively suppressing noise with superior performance and enhanced temporal stability. Full article

(This article belongs to the Topic Visual Computing and Understanding: New Developments and Trends)

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44 pages, 11702 KiB

Open AccessReview

Low-Light Image and Video Enhancement for More Robust Computer Vision Tasks: A Review

by Mpilo M. Tatana, Mohohlo S. Tsoeu and Rito C. Maswanganyi

J. Imaging 2025, 11(4), 125; https://doi.org/10.3390/jimaging11040125 - 21 Apr 2025

Computer vision aims to enable machines to understand the visual world. Computer vision encompasses numerous tasks, namely action recognition, object detection and image classification. Much research has been focused on solving these tasks, but one that remains relatively uncharted is light enhancement (LE). [...] Read more.

Computer vision aims to enable machines to understand the visual world. Computer vision encompasses numerous tasks, namely action recognition, object detection and image classification. Much research has been focused on solving these tasks, but one that remains relatively uncharted is light enhancement (LE). Low-light enhancement (LLE) is crucial as computer vision tasks fail in the absence of sufficient lighting, having to rely on the addition of peripherals such as sensors. This review paper will shed light on this (focusing on video enhancement) subfield of computer vision, along with the other forementioned computer vision tasks. The review analyzes both traditional and deep learning-based enhancers and provides a comparative analysis on recent models in the field. The review also analyzes how popular computer vision tasks are improved and made more robust when coupled with light enhancement algorithms. Results show that deep learners outperform traditional enhancers, with supervised learners obtaining the best results followed by zero-shot learners, while computer vision tasks are improved with light enhancement coupling. The review concludes by highlighting major findings such as that although supervised learners obtain the best results, due to a lack of real-world data and robustness to new data, a shift to zero-shot learners is required. Full article

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

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16 pages, 2833 KiB

Open AccessArticle

Evolution of Lung Disease Studied by Computed Tomography in Adults with Cystic Fibrosis Treated with Elexacaftor/Tezacaftor/Ivacaftor

by Susana Hernández-Muñiz, Paloma Caballero, Adrián Peláez, Marta Solís-García, Carmen de Benavides, Javier Collada, Ignacio Díaz-Lorenzo, Cristina Zorzo, Rosa Mar Gómez-Punter and Rosa María Girón

J. Imaging 2025, 11(4), 124; https://doi.org/10.3390/jimaging11040124 - 21 Apr 2025

Elexacaftor–tezacaftor–ivacaftor (ETI) has shown clinical and spirometric benefits in cystic fibrosis (CF). CT remains a vital tool for diagnosing and monitoring structural lung disease. This study aimed to assess the evolution of lung disease, as evaluated through CT, in adults with CF after [...] Read more.

Elexacaftor–tezacaftor–ivacaftor (ETI) has shown clinical and spirometric benefits in cystic fibrosis (CF). CT remains a vital tool for diagnosing and monitoring structural lung disease. This study aimed to assess the evolution of lung disease, as evaluated through CT, in adults with CF after at least one year of ETI treatment. This ambispective observational analysis assessed lung CT scans performed before initiating ETI and after at least one year of treatment, using the modified Bhalla scoring system. For those patients with an earlier CT scan, a pre-treatment phase analysis was performed. Epidemiological, clinical, and functional parameters were evaluated. Results: Sixty-two patients were included (35 males, median age 30.4 ± 7.87 years). After at least one year of ETI, significant improvements were observed in the global CT Bhalla score (12.2 ± 2.8 vs. 14.0 ± 2.8), peribronchial thickening (1.4 ± 0.6 vs. 1.0 ± 0.4), and mucus plugging (1.6 ± 0.7 vs. 0.8 ± 0.6) (p < 0.001). Spirometry parameters increased significantly: the percentage of the predicted forced expiratory volume in the first second (ppFEV₁) increased from 66.5 ± 19.8 to 77.0 ± 20.4 (p = 0.005) and forced vital capacity (ppFVC) from 80.6 ± 16.4 to 91.6 ± 14.1 (p < 0.001). Additionally, body mass index showed a significant increase. A moderate correlation was found between the Bhalla score and spirometry results. In the pre-treatment phase (n = 52), mucus plugging demonstrated a significant worsening, whereas global CT score, other subscores, and spirometry did not change significantly. Conclusions: In adults with CF, after at least one year of ETI, a significant improvement in structural lung disease was achieved, as reflected by the CT Bhalla score. Full article

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

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17 pages, 8594 KiB

Open AccessArticle

Evolutionary-Driven Convolutional Deep Belief Network for the Classification of Macular Edema in Retinal Fundus Images

by Rafael A. García-Ramírez, Ivan Cruz-Aceves, Arturo Hernández-Aguirre, Gloria P. Trujillo-Sánchez and Martha A. Hernandez-González

J. Imaging 2025, 11(4), 123; https://doi.org/10.3390/jimaging11040123 - 21 Apr 2025

Early detection of diabetic retinopathy is critical for preserving vision in diabetic patients. The classification of lesions in Retinal fundus images, particularly macular edema, is an essential diagnostic tool, yet it presents a significant learning curve for both novice and experienced ophthalmologists. To [...] Read more.

Early detection of diabetic retinopathy is critical for preserving vision in diabetic patients. The classification of lesions in Retinal fundus images, particularly macular edema, is an essential diagnostic tool, yet it presents a significant learning curve for both novice and experienced ophthalmologists. To address this challenge, a novel Convolutional Deep Belief Network (CDBN) is proposed to classify image patches into three distinct categories: two types of macular edema—microhemorrhages and hard exudates—and a healthy category. The method leverages high-level feature extraction to mitigate issues arising from the high similarity of low-level features in noisy images. Additionally, a Real-Coded Genetic Algorithm optimizes the parameters of Gabor filters and the network, ensuring optimal feature extraction and classification performance. Experimental results demonstrate that the proposed CDBN outperforms comparative models, achieving an F1 score of 0.9258. These results indicate that the architecture effectively overcomes the challenges of lesion classification in retinal images, offering a robust tool for clinical application and paving the way for advanced clinical decision support systems in diabetic retinopathy management. Full article

(This article belongs to the Special Issue Advances in Biomedical Image Processing and Artificial Intelligence for Computer-Aided Diagnosis in Medicine)

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15 pages, 3751 KiB

Open AccessArticle

Classification of Parotid Tumors with Robust Radiomic Features from DCE- and DW-MRI

by Francesca Angelone, Silvia Tortora, Francesca Patella, Maria Chiara Bonanno, Maria Teresa Contaldo, Mario Sansone, Gianpaolo Carrafiello, Francesco Amato and Alfonso Maria Ponsiglione

J. Imaging 2025, 11(4), 122; https://doi.org/10.3390/jimaging11040122 - 17 Apr 2025

This study aims to evaluate the role of MRI-based radiomic analysis and machine learning using both DWI with multiple B-values and dynamic contrast-enhanced T1-weighted sequences to differentiate benign (B) and malignant (M) parotid tumors. Patients underwent DCE- and DW-MRI. An expert radiologist performed [...] Read more.

This study aims to evaluate the role of MRI-based radiomic analysis and machine learning using both DWI with multiple B-values and dynamic contrast-enhanced T1-weighted sequences to differentiate benign (B) and malignant (M) parotid tumors. Patients underwent DCE- and DW-MRI. An expert radiologist performed the manual selection of 3D ROIs. Classification of malignant vs. benign parotid tumors was based on radiomic features extracted from DCE-based and DW-based parametric maps. Care was taken in robustness evaluation and the no-bias selection of features. Several classifiers were employed. Sensitivity and specificity ranged from 0.6 to 0.8. The combination of LASSO + neural networks achieved the highest performance (0.76 sensitivity and 0.75 specificity). Our study identified a few robust DCE-based radiomic features with respect to ROI selection that can effectively be adopted in classifying malignant vs. benign parotid tumors. Full article

(This article belongs to the Section Medical Imaging)

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31 pages, 203525 KiB

Open AccessArticle

Implementation of Chaotic Synchronization and Artificial Neural Networks in Modified OTP Scheme for Image Encryption

by Hristina Stoycheva, Georgi Mihalev, Stanimir Sadinov and Krasen Angelov

J. Imaging 2025, 11(4), 121; https://doi.org/10.3390/jimaging11040121 - 17 Apr 2025

This paper presents a modified image encryption scheme based on the OTP (One-Time Pad) algorithm, consisting of chaotic synchronization and artificial neural networks (ANNs) for improved security and efficiency. The scheme uses chaotic synchronization based on feedback control to create complex and unique [...] Read more.

This paper presents a modified image encryption scheme based on the OTP (One-Time Pad) algorithm, consisting of chaotic synchronization and artificial neural networks (ANNs) for improved security and efficiency. The scheme uses chaotic synchronization based on feedback control to create complex and unique encryption keys. Additionally, ANNs are used to approximate time functions, creating a neural encoding key, which adds an additional layer of complexity to the encryption process. The proposed scheme integrates static, chaotic, and neural keys in a multilayer structure, providing high resistance against statistical and cryptographic attacks. The results show that the proposed methodology achieves entropy values close to the theoretical maximum, effectively destroys the correlation between pixels, and demonstrates high sensitivity to variations in the input data. The proposed scheme shows very good feasibility in terms of both security and efficiency, which gives a reliable solution for secure image transmission and storage. This is proven by a study of resistance to various crypto–graphic attacks such as brute force attack, differential attack, noise and data cut attacks, key sensitivity, and computational complexity. Full article

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

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14 pages, 733 KiB

Open AccessArticle

RGB Color Space-Enhanced Training Data Generation for Cucumber Classification

by Hotaka Hoshino, Takuya Shindo, Takefumi Hiraguri and Nobuhiko Itoh

J. Imaging 2025, 11(4), 120; https://doi.org/10.3390/jimaging11040120 - 17 Apr 2025

Cucumber farmers classify harvested cucumbers based on specific criteria before they are introduced to the market. During peak harvesting periods, farmers must process a large volume of cucumbers; however, the classification task requires specialized knowledge and experience. This expertise-dependent process poses a significant [...] Read more.

Cucumber farmers classify harvested cucumbers based on specific criteria before they are introduced to the market. During peak harvesting periods, farmers must process a large volume of cucumbers; however, the classification task requires specialized knowledge and experience. This expertise-dependent process poses a significant challenge, as it prevents untrained individuals, including hired workers, from effectively assisting in classification, thereby necessitating that farmers perform the task themselves. To address this issue, this study aims to develop a classification system that enables individuals, regardless of their level of expertise, to accurately classify cucumbers. The proposed system employs a convolutional neural network (CNN) to process cucumber images and generate classification results. The CNN used in this study consists of a total of 11 layers: 2 convolution layers, 2 pooling layers, 3 dense layers, and 4 dropout layers. To facilitate the widespread adoption of this system, improving classification accuracy is imperative. In this paper, we propose a method for embedding information related to cucumber length, bend, and thickness into the background space of cucumber images when creating training data. Specifically, this method encodes these attributes into the RGB color space, allowing the background color to vary based on the cucumber’s length, bend, and thickness. The effectiveness of the proposed method is validated through an evaluation of multi-class classification metrics, including accuracy, recall, precision, and F-measure, using cucumbers classified based on the criteria established by an actual agricultural cooperative. The experimental results demonstrate that the proposed method improves these evaluation metrics, thereby enhancing the overall performance of the system.Specifically, the proposed method achieved 79.1% accuracy, while the method without RGB color space achieved 70.1% accuracy. This indicates that the proposed method achieves 1.1 times better performance than the conventional method. Full article

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

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22 pages, 13074 KiB

Open AccessReview

Computed Tomography Imaging of Thoracic Aortic Surgery: Distinguishing Life-Saving Repairs from Life-Threatening Complications

by Marco Fogante, Paolo Esposto Pirani, Fatjon Cela, Jacopo Alfonsi, Corrado Tagliati, Liliana Balardi, Giulio Argalia, Marco Di Eusanio and Nicolò Schicchi

J. Imaging 2025, 11(4), 119; https://doi.org/10.3390/jimaging11040119 - 17 Apr 2025

Thoracic aortic pathology encompasses a spectrum of life-threatening conditions that demand prompt diagnosis and intervention. Significant advancements in surgical management, including open repair, endovascular aortic repair, and hybrid techniques, have markedly enhanced patient outcomes. However, these procedures necessitate meticulous imaging follow-up to identify [...] Read more.

Thoracic aortic pathology encompasses a spectrum of life-threatening conditions that demand prompt diagnosis and intervention. Significant advancements in surgical management, including open repair, endovascular aortic repair, and hybrid techniques, have markedly enhanced patient outcomes. However, these procedures necessitate meticulous imaging follow-up to identify potential complications. Computed tomography angiography remains the gold standard for evaluating aortic pathology, guiding surgical planning, and monitoring postoperative changes. A thorough understanding of the characteristic imaging features associated with various aortic surgical techniques is crucial for precise assessment, enhancing postoperative surveillance, and optimizing patient management. Distinguishing between surgical complications and postoperative findings is vital to prevent misdiagnosis. This review examines the imaging characteristics of thoracic aortic diseases and their corresponding surgical interventions, emphasizing the differentiation between expected postoperative findings and true pathological conditions. This approach aims to facilitate accurate diagnosis and effective management of complications, ultimately improving patient care. Full article

(This article belongs to the Section Medical Imaging)

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18 pages, 4568 KiB

Open AccessArticle

Impact of Display Pixel–Aperture Ratio on Perceived Roughness, Glossiness, and Transparency

by Kosei Aketagawa, Midori Tanaka and Takahiko Horiuchi

J. Imaging 2025, 11(4), 118; https://doi.org/10.3390/jimaging11040118 - 16 Apr 2025

Shitsukan, which encompasses the perception of roughness, glossiness, and transparency/translucency, represents the comprehensive visual appearance of objects and plays a crucial role in accurate reproduction across various fields, including manufacturing and imaging technologies. This study experimentally examines the impact of the pixel–aperture ratio [...] Read more.

Shitsukan, which encompasses the perception of roughness, glossiness, and transparency/translucency, represents the comprehensive visual appearance of objects and plays a crucial role in accurate reproduction across various fields, including manufacturing and imaging technologies. This study experimentally examines the impact of the pixel–aperture ratio on the perception of roughness, glossiness, and transparency. A visual evaluation experiment was conducted using natural images presented on stimuli with pixel–aperture ratios of 100% and 6%, employing an RGB sub-pixel array. The results demonstrated that the pixel–aperture ratio significantly affects the perception of glossiness and transparency, with the 100% pixel–aperture ratio producing a statistically significant effect compared to the 6% condition. However, roughness perception varied substantially among the observers, and no statistically significant effect was observed. Nonetheless, when comparing two observer clusters identified through clustering analysis, the cluster favoring the 100% pixel–aperture ratio exhibited “Huge” effect sizes for all perceptual attributes. Additionally, the findings indicate that the degree of influence of pixel–aperture ratio on glossiness and transparency is not constant and can vary depending on individual observer differences and image characteristics. Full article

(This article belongs to the Special Issue Color in Image Processing and Computer Vision)

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14 pages, 3375 KiB

Open AccessArticle

YOLO-Tryppa: A Novel YOLO-Based Approach for Rapid and Accurate Detection of Small Trypanosoma Parasites

by Davide Antonio Mura, Luca Zedda, Andrea Loddo and Cecilia Di Ruberto

J. Imaging 2025, 11(4), 117; https://doi.org/10.3390/jimaging11040117 - 15 Apr 2025

Early detection of Trypanosoma parasites is critical for the prompt treatment of trypanosomiasis, a neglected tropical disease that poses severe health and socioeconomic challenges in affected regions. To address the limitations of traditional manual microscopy and prior automated methods, we propose YOLO-Tryppa, a [...] Read more.

Early detection of Trypanosoma parasites is critical for the prompt treatment of trypanosomiasis, a neglected tropical disease that poses severe health and socioeconomic challenges in affected regions. To address the limitations of traditional manual microscopy and prior automated methods, we propose YOLO-Tryppa, a novel YOLO-based framework specifically engineered for the rapid and accurate detection of small Trypanosoma parasites in microscopy images. YOLO-Tryppa incorporates ghost convolutions to reduce computational complexity while maintaining robust feature extraction and introduces a dedicated P2 prediction head to improve the localization of small objects. By eliminating the redundant P5 prediction head, the proposed approach achieves a significantly lower parameter count and reduced GFLOPs. Experimental results on the public Tryp dataset demonstrate that YOLO-Tryppa outperforms the previous state of the art by achieving an AP50 of 71.3%, thereby setting a new benchmark for both accuracy and efficiency. These improvements make YOLO-Tryppa particularly well-suited for deployment in resource-constrained settings, facilitating more rapid and reliable diagnostic practices. Full article

(This article belongs to the Special Issue Deep Learning in Biomedical Image Segmentation and Classification: Advancements, Challenges and Applications)

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29 pages, 6364 KiB

Open AccessArticle

Face Anti-Spoofing Based on Adaptive Channel Enhancement and Intra-Class Constraint

by Ye Li, Wenzhe Sun, Zuhe Li and Xiang Guo

J. Imaging 2025, 11(4), 116; https://doi.org/10.3390/jimaging11040116 - 10 Apr 2025

Face anti-spoofing detection is crucial for identity verification and security monitoring. However, existing single-modal models struggle with feature extraction under complex lighting conditions and background variations. Moreover, the feature distributions of live and spoofed samples often overlap, resulting in suboptimal classification performance. To [...] Read more.

Face anti-spoofing detection is crucial for identity verification and security monitoring. However, existing single-modal models struggle with feature extraction under complex lighting conditions and background variations. Moreover, the feature distributions of live and spoofed samples often overlap, resulting in suboptimal classification performance. To address these issues, we propose a jointly optimized framework integrating the Enhanced Channel Attention (ECA) mechanism and the Intra-Class Differentiator (ICD). The ECA module extracts features through deep convolution, while the Bottleneck Reconstruction Module (BRM) employs a channel compression–expansion mechanism to refine spatial feature selection. Furthermore, the channel attention mechanism enhances key channel representation. Meanwhile, the ICD mechanism enforces intra-class compactness and inter-class separability, optimizing feature distribution both within and across classes, thereby improving feature learning and generalization performance. Experimental results show that our framework achieves average classification error rates (ACERs) of 2.45%, 1.16%, 1.74%, and 2.17% on the CASIA-SURF, CASIA-SURF CeFA, CASIA-FASD, and OULU-NPU datasets, outperforming existing methods. Full article

(This article belongs to the Section Biometrics, Forensics, and Security)

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22 pages, 3420 KiB

Open AccessArticle

A Hybrid CNN Framework DLI-Net for Acne Detection with XAI

by Shaila Sharmin, Fahmid Al Farid, Md. Jihad, Shakila Rahman, Jia Uddin, Rayhan Kabir Rafi, Radia Hossan and Hezerul Abdul Karim

J. Imaging 2025, 11(4), 115; https://doi.org/10.3390/jimaging11040115 - 10 Apr 2025

Acne is a prevalent skin condition that can significantly impact individuals’ psychological and physiological well-being. Detecting acne lesions is crucial for improving dermatological care and providing timely treatment. Numerous studies have explored the application of deep learning models to enhance the accuracy and [...] Read more.

Acne is a prevalent skin condition that can significantly impact individuals’ psychological and physiological well-being. Detecting acne lesions is crucial for improving dermatological care and providing timely treatment. Numerous studies have explored the application of deep learning models to enhance the accuracy and speed of acne diagnoses. This study introduces a novel hybrid model that combines DeepLabV3 for precise image segmentation with InceptionV3 for classification, offering an enhanced solution for acne detection. The DeepLabV3 model isolates acne lesions and generates accurate segmentation masks, while InceptionV3 efficiently classifies the different types of acne, improving the overall diagnostic accuracy. The model was trained using a custom dataset and evaluated using advanced optimization techniques. The hybrid model achieved exceptional performances with a validation accuracy of 97%, a test accuracy of 97%, an F1 score of 0.97, a precision of 0.97, and a recall of 0.97, surpassing many of the existing baseline models. To enhance its interpretability further, Grad-CAM (Gradient-Weighted Class Activation Mapping) is utilized to visualize the regions of the image that the model focuses on during predictions, providing transparent insights into the decision-making process. This study underscores the transformative potential of AI in dermatology, offering a robust solution for acne detection and classification, which can significantly improve clinical decision making and patient outcomes. Full article

(This article belongs to the Section Medical Imaging)

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19 pages, 28051 KiB

Open AccessArticle

WEDM: Wavelet-Enhanced Diffusion with Multi-Stage Frequency Learning for Underwater Image Enhancement

by Junhao Chen, Sichao Ye, Xiong Ouyang and Jiayan Zhuang

J. Imaging 2025, 11(4), 114; https://doi.org/10.3390/jimaging11040114 - 9 Apr 2025

Underwater image enhancement (UIE) is inherently challenging due to complex degradation effects such as light absorption and scattering, which result in color distortion and a loss of fine details. Most existing methods focus on spatial-domain processing, often neglecting the frequency-domain characteristics that are [...] Read more.

Underwater image enhancement (UIE) is inherently challenging due to complex degradation effects such as light absorption and scattering, which result in color distortion and a loss of fine details. Most existing methods focus on spatial-domain processing, often neglecting the frequency-domain characteristics that are crucial for effectively restoring textures and edges. In this paper, we propose a novel UIE framework, the Wavelet-based Enhancement Diffusion Model (WEDM), which integrates frequency-domain decomposition with diffusion models. The WEDM consists of two main modules: the Wavelet Color Compensation Module (WCCM) for color correction in the LAB space using discrete wavelet transform, and the Wavelet Diffusion Module (WDM), which replaces traditional convolutions with wavelet-based operations to preserve multi-scale frequency features. By combining residual denoising diffusion with frequency-specific processing, the WEDM effectively reduces noise amplification and high-frequency blurring. Ablation studies further demonstrate the essential roles of the WCCM and WDM in improving color fidelity and texture details. Our framework offers a robust solution for underwater visual tasks, with promising applications in marine exploration and ecological monitoring. Full article

(This article belongs to the Special Issue Underwater Imaging (2nd Edition))

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13 pages, 2935 KiB

Open AccessArticle

Recurrence Quantification Analysis for Scene Change Detection and Foreground/Background Segmentation in Videos

by Theodora Kyprianidi, Effrosyni Doutsi and Panagiotis Tsakalides

J. Imaging 2025, 11(4), 113; https://doi.org/10.3390/jimaging11040113 - 8 Apr 2025

This paper presents the mathematical framework of Recurrence Quantification Analysis (RQA) for dynamic video processing, exploring its applications in two primary tasks: scene change detection and adaptive foreground/background segmentation. Originally developed for time series analysis, Recurrence Quantification Analysis (RQA) examines the recurrence of [...] Read more.

This paper presents the mathematical framework of Recurrence Quantification Analysis (RQA) for dynamic video processing, exploring its applications in two primary tasks: scene change detection and adaptive foreground/background segmentation. Originally developed for time series analysis, Recurrence Quantification Analysis (RQA) examines the recurrence of states within a dynamic system. When applied to video streams, RQA detects recurrent patterns by leveraging the temporal dynamics of video frames. This approach offers a computationally efficient and robust alternative to traditional deep learning methods, which often demand extensive training data and high computational power. Our approach is evaluated on three annotated video datasets: Autoshot, RAI, and BBC Planet Earth, where it demonstrates effectiveness in detecting abrupt scene changes, achieving results comparable to state-of-the-art techniques. We also apply RQA to foreground/background segmentation using the UCF101 and DAVIS datasets, where it accurately distinguishes between foreground motion and static background regions. Through the examination of heatmaps based on the embedding dimension and Recurrence Plots (RPs), we show that RQA provides precise segmentation, with RPs offering clearer delineation of foreground objects. Our findings indicate that RQA is a promising, flexible, and computationally efficient approach to video analysis, with potential applications across various domains requiring dynamic video processing. Full article

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

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