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A Comprehensive Review on image Encryption Techniques using Memristor based Chaotic System for Multimedia Application

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IETE JOURNAL OF RESEARCH 2024年第11期70卷 8160-8183页

作者： Kumar, B. Sakthi Revathi, R. Sri Eshwar Coll Engn Dept ECE Coimbatore India KLEF Univ Dept ECE Guntur India

image encryption is one of the most widely used techniques for securing images in trusted and unrestricted public media. However, the drawback is weak security in chaotic encryption algorithms, small key space and hardware implementation complexities. Various appropriate encryption algorithms have been carried out for secure image transmission;it becomes a critical issue to protect image integrity, confidentiality and authenticity. To overcome these issues, this article examines several existing memristor-based image encryption algorithms with various chaotic maps. As a result, this research aims to deliver comprehensive literature on image encryption techniques through memristor to help the researchers. Finally, this survey provides all vital literature on the current image encryption techniques with their benefits, drawbacks, developments, and future directions. We also give a general guideline about cryptography. We conclude that all methods are helpful for real-time image encryption. A comparison has been made between many image processing approaches (conventional and memristor) based on metrics such as the number of pixels change rate (NPCR) with the value of 0.9986, histogram, entropy (7.9993), unified average varying intensity (UACI) achieves the value as 0.4996, energy consumption as 0.32pJ and time complexity as 0.9s. The experimental results of various approaches, key sensitivity and statistical analysis revealed that the survey of memristor-based image encryption schemes provides an effective way to secure image transmission in real-time application.

关键词： deep learning image decryption image encryption image processing Memristor Security

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An effective IoT interface considering an eye-tracking method for autonomous vehicle

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INTERNET OF THINGS 2025年 31卷

作者： Park, Junghoon Ajou Univ Coll Comp & Informat Dept Appl Artificial Intelligence WorldCup Ro 206 Suwon 16499 Gyeonggi Do South Korea

The number of Internet-connected devices is steadily increasing, exceeding 25 billion globally and projected to surpass 50 billion about 6.5 times the world population. At the core of IoT is data collection via sensors, forming big data for AI-driven analysis, optimization, and visualization. A convenient control environment is essential for devices like autonomous vehicles, requiring new interfaces such as touchless eye movement. This research proposes a real-time eye-tracking method using a single web camera, easily installed in cars and integrated with IoT. The system detects gaze by recognizing iris shape, enabling software-only tracking without extra hardware. Experiments show a mean absolute error (MAE) of 3.49 degrees, ensuring accuracy even with head movement. Unlike existing infrared (IR) LED or head-mounted methods, this approach offers a cost-effective, real-time solution. Using lightweight image processing instead of deep learning, the system achieves real-time tracking with low latency, making it ideal for low-power IoT and autonomous vehicles. It is expected to become a next-generation input interface for these applications.

关键词： Autonomous system Eye gaze tracking IoT image processing Interpolation real-time processing

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An Enhanced Lung Cancer Detection Approach Using Dual-Model deep learning Technique

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 835-867页

作者： Sumaia Mohamed Elhassan Saad Mohamed Darwish Saleh Mesbah Elkaffas Department of Information Technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandria21526Egypt College of Computing and Information Technology Arab Academy for ScienceTechnology and Maritime TransportAlexandriaP.O.Box 1029Egypt

Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic *** detection of lung tumors significantly increases the chances of successful treatment and ***,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung ***-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate ***,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor *** overcome these disadvantages,dual-model or multi-model approaches can be *** research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of *** automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung ***8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single *** is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to ***,through the utilization of cutting-edge data augmentation methods,such as deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive *** combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applicati

关键词： Lung cancer detection dual-model deep learning technique data augmentation CNN YOLOv8

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Recent Advances in deep-learning-Based SAR image Target Detection and Recognition

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 6884-6915页

作者： Lang, Ping Fu, Xiongjun Dong, Jian Yang, Huizhang Yin, Junjun Yang, Jian Martorella, Marco Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Beijing Inst Technol Sch Integrated Circuits & Elect Beijing 100081 Peoples R China Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Univ Pisa Dept Informat Engn I-56127 Pisa Italy

Synthetic aperture radar (SAR) image target detection and recognition (SAR-TDR) tasks have become research hot spots in the remote sensing application. These targets include ships, vehicles, aircraft, oil tanks, bridges, and so on. However, with the rapid development of SAR technology and increasingly complex electromagnetic environment, complex characteristics of SAR images bring severe challenges to the accurate SAR-TDR via traditional physical models or manual feature-extraction-based machine learning methods. In recent years, deep learning (DL), as a powerful automatic feature extraction algorithm, has been widely used in the computer vision domain. More specifically, DL has also been introduced into the SAR-TDR tasks and has effectively achieved good performance in terms of accuracy, real-time processing, etc. With the rapid development of DL, SAR image processing, and practical requirements of SAR-TDR in civilian and military domains, it is crucial to conduct a systematic survey on SAR-TDR in the past few years. In this survey article, we mainly conduct a systematic overview of DL-based SAR-TDR literature on two tasks, i.e., target recognition (e.g., ground vehicles, ships, and aircraft) and target detection (e.g., ships, aircraft, change detection, sea surface oil spills, and oil tanks). More specifically, our related works about these topics are also presented to verify the effectiveness of DL-based methods. First, several DL methods (e.g., convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks), commonly used in SAR-TDR, are briefly introduced. Then, a systematic review of DL-based SAR-TDR (including our related works) is presented. Finally, the current challenges and future possible research directions are deeply analyzed and discussed.

关键词： Radar polarimetry Target recognition Synthetic aperture radar Marine vehicles Feature extraction Object detection Aircraft Surveys Accuracy deep learning Automatic target recognition change detection (CD) deep learning (DL) SAR image interpretation target detection

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Lightweight deep learning-based automated concrete surface inspection: Crack classification and pixel-level segmentation

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SMART STRUCTURES AND SYSTEMS 2025年第3期35卷 163-175页

作者： Yang, Dong Cai, Yuan-Yuan Xu, En-Dian Zhang, Jing Yuan, Ye Wang, Yan-Jia Guangzhou Univ Earthquake Engn Res & Test Ctr EERTC Guangzhou Peoples R China Guangzhou Univ Key Lab Earthquake Resistance Earthquake Mitigat & Struct Safety Minist Educ Guangzhou Peoples R China Hefei Univ Technol Dept Civil Engn Hefei Anhui Peoples R China Jinan Univ Dept Mech & Construct Engn Guangzhou Peoples R China Univ Hong Kong Dept Civil Engn Pokfulam Rd Hong Kong Peoples R China

Crack detection is an important measure in the field of structural health monitoring. However, visual crack detection is labor-intensive, time-consuming, inefficient, and expensive. Although image-based detection and processing provides an efficient way for structural crack detection, its accuracy depends on image quality. For engineering structures, especially bridges, the change of light conditions and the difference of surface characteristics of structural components pose a major challenge to traditional crack detection methods. In this paper, a novel crack detection method based on convolutional neural networks is proposed. The development of this method is divided into the following stages. The initial automated crack classification is carried out by using MobileNetV3, and then the improved deepLabv3+ network is used to segment the classified crack image semantically accurately. Finally, the real crack image is used for verification. To verify the proposed method, several conventional deep learning networks are trained and compared. The improved deepLabV3+ integrates MobileNetV3 as its feature extraction backbone and incorporates the convolutional block attention module, which achieves 87.79% average intersection and 93.87% average pixel accuracy on public and real data sets. Compared with traditional models such as VGG16, the proposed method shortens the training time by more than 80% while maintaining high detection accuracy. In addition, the compact parameter configuration and moderate model size make it particularly suitable for deployment on mobile detection devices.

关键词： attention mechanism crack detection improved deepLabv3+ lightweight deep learning semantic segmentation

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Detection and Classification of Mental Stress Using In-Ear Plethysmography and a Vision Transformer

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IEEE SENSORS JOURNAL 2025年第2期25卷 4015-4027页

作者： Barki, Hika Nkenyereye, Lionel Chung, Wan-Young Pukyong Natl Univ Dept AI Convergence Busan 48513 South Korea Pukyong Natl Univ Dept Elect Engn Busan 48513 South Korea

This study addresses the critical need for effective mental stress monitoring, linked to severe health issues like depression and heart disease. We introduce a robust method using in-ear photoplethysmogram (PPG) signals for detecting and classifying stress levels. The objective of this study is to develop a precise stress monitoring technique using advanced signal processing and deep learning. Raw PPG data were collected from 15 subjects undergoing stress-inducing activities in a controlled setting. The data underwent preprocessing and were transformed into image-like time-frequency representations. We employed vision transformer (ViT) models for classification, which were fine-tuned and compared against other state-of-the-art deep learning models. The ViT classifier significantly outperformed existing models, achieving an average accuracy of 97.78% and an F1-score of 97.79%. While the dataset is relatively small, these results suggest a promising direction for stress monitoring by illustrating the potential of combining in-ear PPG signals with ViT models. The study indicates the efficacy of this novel approach for accurate mental stress diagnosis, which could have significant implications for mental health applications. Future work will focus on validating these findings with a larger sample size and exploring the integration of this technology into wearable devices for real-world stress monitoring.

关键词： Human factors Sensors Accuracy Monitoring Hardware Biomedical monitoring Heart rate variability Transformers real-time systems deep learning Classification filtering mental stress photoplethysmogram (PPG) time-frequency analysis vision transformer (ViT)

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real-time deconvolution of light fields through pixel selection in the point-spread-function and direct inversion of the image formation process

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APPLIED OPTICS 2025年第3期64卷 578-587页

作者： Eckstein, Viktor Schmid-schirling, Tobias Carl, Daniel Wallrabe, Ulrike Fraunhofer Inst Phys Measurement Tech IPM Freiburg Germany Univ Freiburg Dept Microsyst Engn IMTEK Lab Microactuators Freiburg Germany

This paper explores the optimization of light field deconvolution, a key process in image processing that reconstructs a 3D object space or a 2D refocus plane from a light field. Despite the critical role of deconvolution in light field technology, existing methods are often slow, computationally intensive, and unsuitable for real-time processing. Existing algorithms, such as the Richardson-Lucy approach, while groundbreaking, still suffer performance limitations due to their iterative nature and high computational costs. Central to our approach is the strategic selection of influential pixels within the point-spread-function, reducing redundant computations by focusing only on pixels contributing to a significant portion of the point-spread-function's total intensity. In addition, we explore the potential to directly invert the image formation model, bypass iterative computations, and further accelerate the deconvolution process. Our findings reveal notable improvements in computational efficiency, with some of our methods achieving real-time performance. The reconstruction quality, measured using metrics such as the mean squared error, remained comparable to existing approaches, indicating a favorable balance between speed and reconstruction quality. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Biomedical imaging deep learning Microlens arrays Neural networks Optical design software Plenoptic imaging

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Adaptive specular reflection removal in light field microscopy using multi-polarization hybrid illumination and deep learning

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OPTICS AND LASERS IN ENGINEERING 2025年 186卷

作者： Shi, Wenqi Quan, Hongda Kong, Lingbao Fudan Univ Shanghai Engn Res Ctr Ultraprecis Opt Mfg Sch Informat Sci & Technol Shanghai 200433 Peoples R China

Specular highlights pose a significant challenge in light field microscopy (LFM), leading to information loss and inaccurate observations, especially on reflective surfaces. Existing methods for specular highlight removal often suffer from high computational complexity, limited applicability and extended processing times. To address these limitations, this paper introduces an adaptive hybrid illumination scheme that combines multiple polarized light sources with a deep learning-based control system to dynamically modulate illumination and eliminate specular highlights. This method effectively removes highlight reflections and provides uniform illumination without complex optical setups or mechanical components. By leveraging various polarization angles and utilizing precise electronic control through a neural network, the system dynamically adjusts lighting in real-time, achieving uniform illumination and superior image quality. Experimental results show that the proposed method effectively eliminates specular highlights, significantly improves 3D reconstruction accuracy and reduces processing time to <0.4 s (at least twice as fast as traditional approaches). This system offers a promising solution for applications requiring high-speed, high-precision imaging, such as biological analysis, industrial inspection, and materials research, providing an efficient and effective alternative for specular reflection removal in LFM.

关键词： Specular reflection removal Multi-polarization Adaptive hybrid modulated illumination deep learning Light field microscopy Three-dimensional reconstruction

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Coupling deep and handcrafted features to assess smile genuineness

Coupling deep and handcrafted features to assess smile genui...

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Conference on real-time image processing and deep learning

作者： Pawlus, Benedykt Smolka, Bogdan Kawulok, Jolanta Kawulok, Michal Fac Automat Control Elect & Comp Sci Gliwice Poland

ISBN: (纸本)9781510673878;9781510673861

Assessing smile genuineness from video sequences is a vital topic concerned with recognizing facial expression and linking them with the underlying emotional states. There have been a number of techniques proposed underpinned with handcrafted features, as well as those that rely on deep learning to elaborate the useful features. As both of these approaches have certain benefits and limitations, in this work we propose to combine the features learned by a long short-term memory network with the features handcrafted to capture the dynamics of facial action units. The results of our experiments indicate that the proposed solution is more effective than the baseline techniques and it allows for assessing the smile genuineness from video sequences in real-time.

关键词： Facial expression recognition smile genuineness deep learning handcrafted features facial action units

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Non-contact vibration sensor using deep learning and image processing

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MEASUREMENT 2021年 183卷 109823-109823页

作者： Guo, Jinquan Wu, Xinran Liu, Jiantao Wei, Tieping Yang, Xiaoxiang Yang, Xinyi He, Bingwei Zhang, Weihao Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350108 Peoples R China Xiamen Port Holding Grp Co Ltd Xiamen 361000 Fujian Peoples R China Fujian Univ Technol Sch Mech & Automat Engn Fuzhou 350118 Peoples R China Quanzhou Normal Univ Quanzhou 362000 Fujian Peoples R China Xiamen Inst Environm Sci Xiamen 361000 Fujian Peoples R China

This paper proposes a non-contact vibration measurement method based on deep learning and image processing. The deep learning method is used to realize the automatic and efficient selection of effective pixels and the optical flow method is used to extract vibration signals to realize non-contact and targetless visual vibration measurement. In this study, a carbon plate board and aluminum C-beam structure were measured and verified under artificial and non-human excitation in a laboratory environment. Additionally, bridge and cable structures in an outdoor environment were selected as measurement targets to verify the reliability of the proposed method. This paper compares the experimental results of Canny and Sobel edge detection algorithms and deep learning methods to verify the efficiency of deep learning. The results demonstrate that our method is robust, even under real-world unfavorable conditions, meaning it can serve as a novel measurement method in the field of vibration measurement.

关键词： Vibration measurement Optical flow deep learning Convolutional neural network Photogrammetry Computer vision Non-contact measurement

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