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Scanning imaging through the scattering medium based on the guided filter assisted by a WTMM and NLM

APPLIED OPTICS 2025年第10期64卷 2408-2418页

作者： Zhang, Yiyang Ding, Zhewen Li, Runze Meng, Xianglei Shi, Yan Zhan, Chunlian Zhao, Chunliu China Jiliang Univ Coll Opt & Elect Technol Hangzhou 310018 Zhejiang Peoples R China State Adm Market Regulat Key Lab Light Sensing & Image Metrol Hangzhou 310018 Zhejiang Peoples R China Zhejiang Prov Key Lab Optoelect Metrol & Precis In Hangzhou 310018 Zhejiang Peoples R China

In a scanning imaging system through a scattering medium, the quality of the imaging result is related to the energy distribution of the focusing point. In actual imaging, the energy of the focusing point cannot be perfectly concentrated. The scattering noise is always surrounded by the focusing point, which reduces the signal-to-noise ratio and results in poor image quality. To improve the quality of imaging, further processing of the imaging results is required, while most conventional image processing methods can only achieve one of the goals such as noise reduction, image smoothing, edge sharpening, or maintaining image integrity. In this paper, a scanning imaging system through the scattering medium based on an adaptive guided filter assisted by the wavelet transform modulus maximum (WTMM) and non-local mean (NLM) is proposed for the first time, to the best of our knowledge, which can make the imaging results simultaneously have the advantages of low noise, high contrast, and clear details. To verify the validity of the proposed method, a scanning imaging system through the scattering medium was setup. Transmissive imaging was performed at different positions from the focal plane. The experimental results show that the background noise is significantly restrained, single-pixel response and edge continuity are good, and details are clear with the proposed method. Compared with the traditional methods and the deep learning methods, the proposed method can improve the PSNR and SSIM by up to 10.68 dB, 0.75 and 5.34 dB, 0.72, respectively. Finally, the performance of the proposed method in high-noise environments, its application in the field of real-time imaging, and future improvements are discussed. The method proposed in this paper can effectively improve the quality of scanning imaging results through the scattering medium, which is expected to promote the application of this technology in endoscopic imaging and other fields. (c) 2025 Optica Publishing Group. A

关键词： deep learning Imaging systems Imaging techniques real time imaging Scattering media Wavelet transforms

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A real-time Tracking Approach for Moving Objects Based on an Integrated Algorithm of YOLOv7 and SORT

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2025年第4期34卷

作者： Wang, Lei Liu, Tongtong Yellow River Conservancy Tech Inst Coll Elect Engn Kaifeng 475004 Henan Peoples R China

Mobile target tracking remains a significant issue in smart cities. Due to complex changes in time and space of targets, real-time tracking remains a challenging problem. As a result, this paper proposes a real-time tracking approach for moving objects by combining the advantages of YOLOv7 and SORT algorithms. First, we use the YOLOv7 algorithm for object detection, which has the characteristics of high accuracy and efficiency. Then, we apply the SORT algorithm to the target tracking stage, which estimates and updates the target state through Kalman filtering. The collaborative function of the two parts is expected to achieve high-quality tracking of moving targets. Besides, this paper also demonstrates experiments and analysis on image datasets. The experimental results show that the proposed algorithm has achieved good performance in real-time tracking of moving targets. Compared with traditional methods, it can more accurately predict the position and trajectory of targets and has better real-time performance. In addition, the proposed algorithm is equally effective for target tracking in complex scenes, such as multi-target tracking and target occlusion. Future research can further optimize the performance of algorithms to cope with more complex scenarios and problems.

关键词： Moving target tracking real-time image processing YOLOv7 Kalman filtering deep learning

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Improving Cerebrovascular Imaging with deep learning: Semantic Segmentation for time-of-Flight Magnetic Resonance Angiography Maximum Intensity Projection image Enhancement

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APPLIED SCIENCES-BASEL 2025年第6期15卷 3034-3034页

作者： Yamada, Tomonari Yoshimura, Takaaki Ichikawa, Shota Sugimori, Hiroyuki Hokkaido Univ Grad Sch Hlth Sci Sapporo 0600812 Japan Hokkaido Univ Fac Hlth Sci Dept Hlth Sci & Technol Sapporo 0600812 Japan Hokkaido Univ Hosp Dept Med Phys Sapporo 0608648 Japan Hokkaido Univ Fac Med Global Ctr Biomed Sci & Engn Sapporo 0608638 Japan Hokkaido Univ Fac Med Clin AI Human Resources Dev Program Sapporo 0608638 Japan Niigata Univ Fac Med Sch Hlth Sci Dept Radiol Technol Niigata 9518518 Japan Niigata Univ Inst Res Adm Niigata 9502181 Japan Hokkaido Univ Fac Hlth Sci Dept Biomed Sci & Engn Sapporo 0600812 Japan

Magnetic Resonance Angiography (MRA) is widely used for cerebrovascular assessment, with time-of-Flight (TOF) MRA being a common non-contrast imaging technique. However, maximum intensity projection (MIP) images generated from TOF-MRA often include non-essential vascular structures such as external carotid branches, requiring manual editing for accurate visualization of intracranial arteries. This study proposes a deep learning-based semantic segmentation approach to automate the removal of these structures, enhancing MIP image clarity while reducing manual workload. Using deepLab v3+, a convolutional neural network model optimized for segmentation accuracy, the method achieved an average Dice Similarity Coefficient (DSC) of 0.9615 and an Intersection over Union (IoU) of 0.9261 across five-fold cross-validation. The developed system processed MRA datasets at an average speed of 16.61 frames per second, demonstrating real-time feasibility. A dedicated software tool was implemented to apply the segmentation model directly to DICOM images, enabling fully automated MIP image generation. While the model effectively removed most external carotid structures, further refinement is needed to improve venous structure suppression. These results indicate that deep learning can provide an efficient and reliable approach for automated cerebrovascular image processing, with potential applications in clinical workflows and neurovascular disease diagnosis.

关键词： semantic segmentation magnetic resonance angiography (MRA) deep learning

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Port obstacle detection algorithm based on image adaptive enhancement

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SIGNAL image AND VIDEO processing 2025年第6期19卷 1-18页

作者： Jiang, Hao Guo, Zijian Xu, Xinglu Zhang, Zhongyi Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Liaoning Peoples R China

When port robots perform inspection tasks, due to the diversity of shapes and categories of road obstacles, missed detections and false detections are prone to occur. At the same time, to meet the needs of robots for safe and normal operation in complex environments, an image-adaptive enhancement-based algorithm for port area environment target obstacle detection and avoidance is proposed. Firstly, in the preprocessing stage, hyperparameter optimization is introduced for the brightness, contrast, and edge clarity of the image to be detected for adaptive enhancement;secondly, a multi-scale detail enhancement module is introduced in yolov8 to enable the network to focus on high-frequency detail information of different scales to solve the problem of unclear image reconstruction edges;then, for the problem of insufficient utilization of scale information, a more efficient and lightweight multi-dimensional collaborative attention mechanism deep convolution module is used to learn the relationship between each channel and spatial position information in the image, and at the same time fuse these information to improve the reconstruction ability of the image;finally, a Transformer module is connected at the head to alleviate the interference of image noise and light pollution on target detection, enhancing the accuracy and robustness of the algorithm. Experiments are carried out by selecting some images from the public dataset and the self-built dataset. The proposed algorithm improves the detection accuracy of the port area environment by 2.1% compared with YOLOv8, reaching 90.1%;the recall rate is improved by 2.7%, reaching 91.2%, and the speed is improved by 6.1%, reaching 70.1FPS;it meets the actual requirements of real-time detection and avoidance of inspection robots

关键词： Adaptive enhancement real-time detection Inspection robots Machine vision deep learning

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Denet: an effective and lightweight real-time semantic segmentation network for coal flow monitoring

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JOURNAL OF real-time image processing 2025年第1期22卷 1-16页

作者： Shao, Xiaoqiang Lyu, Zhiyue Li, Hao Liu, Mingqian Han, Zehui Xian Univ Sci & Technol Xian Peoples R China

Automatic extraction of coal flow region of coal mine belt conveyor plays an important role in coal flow monitoring, and real-time control of belt speed through real-time accurate monitoring of coal flow, which realizes the purpose of energy saving and consumption reduction of belt conveyor. In this paper, a real-time semantic segmentation network with detail enhancement for pixel-level coal flow monitoring, called DENet, is proposed. First, to ensure the strong real-time performance of the network, a two-branch coding structure is used to extract the semantic information and spatial detail information. Second, to improve the feature representation of spatial detail information, we design the Parameter-free Attention-Guided Enhancement Module (PF-AGEM) and the detail enhancement module (DEM), which fully integrate the semantic information features in the semantic branch into the detail branch and further enhance the detail features. Third, we design the multi-scale channel attention (MSCA) module in the semantic branch to extract the semantic information features of small targets earlier in the high-resolution feature maps, which solves the problem that the semantic information features of small targets are easily lost in the low-resolution feature maps. Finally, we propose a selective feature fusion module (FFM) to better realize the fusion of semantic information and spatial detail information. Experimental results show that the proposed DENet achieves a mean intersection over union (mIoU) of 96.23% at 87.1 frames per second (FPS) on the Coal Flow Segmentation (CFS) dataset and 74.9% mIoU at 207 FPS on the Camvid dataset, which is competitive with the state-of-the-art real-time semantic segmentation models.

关键词： Coal flow monitoring image segmentation deep learning real-time

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deep learning Approaches for Morphological Classification of Intestinal Organoids

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IEEE ACCESS 2025年 13卷 62267-62287页

作者： Cicceri, Giovanni Di Bella, Sebastiano Di Franco, Simone Stassi, Giorgio Todaro, Matilde Vitabile, Salvatore Univ Palermo Dept Biomed Neurosci & Adv Diagnost BiND I-90127 Palermo Italy Univ Palermo Dept Precis Med Med Surg & Crit Care MePreCC I-90127 Palermo Italy Univ Palermo Dept Hlth Promot Sci Internal Med & Med Specialties ProMISE I-90127 Palermo Italy

Organoids, derived from primary donor or stem cells, closely replicate the composition and function of their in vivo counterparts. This quality makes them a reliable model for validating hypotheses on disease-related biological processes and mechanisms. To date, the classification of organoids is performed manually by microscope and, therefore, in a data-driven application, is time-consuming, inaccurate, and difficult to morphological analysis process. The use of deep learning (DL) in organoid image analysis becomes crucial to handle complexity, variability, and large amounts of data efficiently and accurately, overcoming the limitations of traditional image processing approaches. In this paper, five CNN-based DL models such as MobileNet, DenseNet, ResNet, Inception, VGG, and the very recent Vision Transformers (ViT) were analyzed using a publicly available dataset for the morphological classification of intestinal organoids. Additionally, traditional ML models, such as SVM and RF, were tested for comparison using a feature set similar to conventional image processing tools. The systematic performance evaluation is designed to guide users in choosing the most suitable model for processing organoid images. Among all models, ViT achieved the highest accuracy of 86.95%, demonstrating its effectiveness in organoid classification. Inception and DenseNet also exhibited strong performance, with accuracy values of 86.10% and 86.47%, respectively. Rather, SVM and RF performed significantly worse, showing an accuracy approximately 20% lower than the selected DL models. Considering efficiency, ViT had the highest accuracy but required more resources (0.0437 sec/image, 343 MB), while MobileNet, the lightest model (35.6 MB), had the fastest inference time (0.0063 sec/image). The findings highlight the potential of DL models in enhancing the accuracy of organoid classification while emphasizing the importance of balancing performance with computational efficiency for real-time ap

关键词： Organoids Accuracy Tumors Computational modeling Analytical models Morphology Biological system modeling Transformers Three-dimensional displays Support vector machines deep learning image classification intestinal organoids stem cells cancer

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A comparison of techniques to improve pulmonary EIT image resolution using a database of simulated EIT images

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2025年 460卷

作者： Howard, Kyler Rocheleau, Chris Overton, Trevor Nava, Joel Barraza Faldet, Mason Moen, Kristina Soller, Summer Stephens, Tyler van de Lagemaat, Esther Wijesinghe, Natalie Dolloff, Kaylee Wong da Rosa, Nilton Barbosa Mueller, Jennifer L. Colorado State Univ Sch Biomed Engn Ft Collins CO 80523 USA Colorado State Univ Dept Math Ft Collins CO 80523 USA

Inherently low spatial resolution is a well-known challenge in electrical impedance tomography image reconstruction. Various approaches such as the use of spatial priors and post-processing techniques have been proposed to improve the resolution, but in the literature, comparisons using a common dataset representative of clinical images have not been considered. Here, we consider a database of 81,710 simulated EIT datasets constructed from pulmonary CT scans of 89 infants. Four techniques for improved image resolution and several combinations thereof are proposed and compared quantitatively on 16,341 known test cases reserved from the database. The techniques include an end-to-end deep learning reconstruction approach, post-processing of real-time one-step Gauss-Newton (GN) reconstructions using machine learning, post-processing using the Schur complement method, the use of an initial guess for the one-step GN method derived from the image database, and a method that makes use of the eigenfunctions of the principal component analysis of image vectors in the database. All methods resulted in improved metrics of error measurement compared to the Newton one-step error reconstruction method used as the basis for comparison.

关键词： Electrical impedance tomography deep learning Pulmonary imaging Inverse problems

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A Review on the Applications of Machine learning and deep learning Algorithms for image Recognition 4

A Review on the Applications of Machine Learning and Deep Le...

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4th International conference on Sentiment Analysis and deep learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Modern image recognition has experienced dramatic improvements because of Machine learning and deep learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition. © 2025 IEEE.

关键词： Adversarial machine learning

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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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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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