Special Issues
Table of Content

New Trends in Image Processing

Submission Deadline: 01 August 2025 View: 1200 Submit to Special Issue

Guest Editors

Full Prof. Hyeonjoon Moon

Email: hmoon@sejong.ac.kr

Affiliation: Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea

Homepage:

Research Interests: image processing, biometrics, artificial intelligence and machine learning

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Rearch Professor Lien Minh Dang

Email: minhdl@sejong.ac.kr

Affiliation: Department of Information and Communication Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea

Homepage:

Research Interests: deep learning; object detection; NLP; pattern recognition; computer vision

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Dr. Tri-Hai Nguyen

Email: hai.nguyentri@vlu.edu.vn

Affiliation: Faculty of Information Technology, School of Technology, Van Lang University, Ho Chi Minh City70000, Vietnam

Homepage:

Research Interests: Algorithms; Reinforcement Learning; Swarm Intelligence; UAV; Resource Allocation; Machine Learning

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Summary

In recent years, the landscape of image and video processing has undergone a transformative shift, driven by advancements in methodologies and technologies across several domains, including computer vision, 3D modeling, computer graphics, and multimedia. The rapid evolution of these fields has spurred the development of innovative applications and techniques, enhancing our ability to interpret and interact with visual data in unprecedented ways. Particularly, the rise of machine learning, and more specifically deep learning, has revolutionized image processing by enabling the efficient analysis of massive datasets, leading to the discovery of new patterns and the creation of sophisticated analytical procedures.

 

This Special Issue seeks to highlight the latest breakthroughs and applications in image and video processing, with a dual focus. Firstly, it explores novel uses of cutting-edge devices for data acquisition and visualization, such as CCTV cameras, 3D scanners, virtual reality (VR) glasses, and robotics, demonstrating how these technologies are reshaping the field. Secondly, it delves into new methodologies for processing large-scale datasets, leveraging modern pattern recognition and machine learning techniques, including deep learning and hypergraph learning, to advance the state of the art in image analysis.


Keywords

3D models processing; Augmented and virtual reality applications; Robotic applications; RGBD analysis; Advanced image enhancement; De-noising and low-light enhancement; Advanced image classification and retrieval; Semantic segmentation; Image processing.

Published Papers


  • Open Access

    ARTICLE

    A Mask-Guided Latent Low-Rank Representation Method for Infrared and Visible Image Fusion

    Kezhen Xie, Syed Mohd Zahid Syed Zainal Ariffin, Muhammad Izzad Ramli
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063469
    (This article belongs to the Special Issue: New Trends in Image Processing)
    Abstract Infrared and visible image fusion technology integrates the thermal radiation information of infrared images with the texture details of visible images to generate more informative fused images. However, existing methods often fail to distinguish salient objects from background regions, leading to detail suppression in salient regions due to global fusion strategies. This study presents a mask-guided latent low-rank representation fusion method to address this issue. First, the GrabCut algorithm is employed to extract a saliency mask, distinguishing salient regions from background regions. Then, latent low-rank representation (LatLRR) is applied to extract deep image features, enhancing More >

  • Open Access

    ARTICLE

    Multimodal Convolutional Mixer for Mild Cognitive Impairment Detection

    Ovidijus Grigas, Robertas Damaševičius, Rytis Maskeliūnas
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.064354
    (This article belongs to the Special Issue: New Trends in Image Processing)
    Abstract Brain imaging is important in detecting Mild Cognitive Impairment (MCI) and related dementias. Magnetic Resonance Imaging (MRI) provides structural insights, while Positron Emission Tomography (PET) evaluates metabolic activity, aiding in the identification of dementia-related pathologies. This study integrates multiple data modalities—T1-weighted MRI, Pittsburgh Compound B (PiB) PET scans, cognitive assessments such as Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR) and Functional Activities Questionnaire (FAQ), blood pressure parameters, and demographic data—to improve MCI detection. The proposed improved Convolutional Mixer architecture, incorporating B-cos modules, multi-head self-attention, and a custom classifier, achieves a classification accuracy of 96.3% More >

  • Open Access

    ARTICLE

    Robust Alzheimer’s Patient Detection and Tracking for Room Entry Monitoring Using YOLOv8 and Cross Product Analysis

    Praveen Kumar Sekharamantry, Farid Melgani, Roberto Delfiore, Stefano Lusardi
    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4215-4238, 2025, DOI:10.32604/cmc.2025.062686
    (This article belongs to the Special Issue: New Trends in Image Processing)
    Abstract Recent advances in computer vision and artificial intelligence (AI) have made real-time people counting systems extremely reliable, with experts in crowd control, occupancy supervision, and security. To improve the accuracy of people counting at entry and exit points, the current study proposes a deep learning model that combines You Only Look Once (YOLOv8) for object detection, ByteTrack for multi-object tracking, and a unique method for vector-based movement analysis. The system determines if a person has entered or exited by analyzing their movement concerning a predetermined boundary line. Two different logical strategies are used to record… More >

  • Open Access

    ARTICLE

    Local Content-Aware Enhancement for Low-Light Images with Non-Uniform Illumination

    Qi Mu, Yuanjie Guo, Xiangfu Ge, Xinyue Wang, Zhanli Li
    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4669-4690, 2025, DOI:10.32604/cmc.2025.058495
    (This article belongs to the Special Issue: New Trends in Image Processing)
    Abstract In low-light image enhancement, prevailing Retinex-based methods often struggle with precise illumination estimation and brightness modulation. This can result in issues such as halo artifacts, blurred edges, and diminished details in bright regions, particularly under non-uniform illumination conditions. We propose an innovative approach that refines low-light images by leveraging an in-depth awareness of local content within the image. By introducing multi-scale effective guided filtering, our method surpasses the limitations of traditional isotropic filters, such as Gaussian filters, in handling non-uniform illumination. It dynamically adjusts regularization parameters in response to local image characteristics and significantly integrates… More >

  • Open Access

    ARTICLE

    Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

    Yawar Abbas, Aisha Ahmed Alarfaj, Ebtisam Abdullah Alabdulqader, Asaad Algarni, Ahmad Jalal, Hui Liu
    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4759-4776, 2025, DOI:10.32604/cmc.2025.059224
    (This article belongs to the Special Issue: New Trends in Image Processing)
    Abstract Human Activity Recognition (HAR) in drone-captured videos has become popular because of the interest in various fields such as video surveillance, sports analysis, and human-robot interaction. However, recognizing actions from such videos poses the following challenges: variations of human motion, the complexity of backdrops, motion blurs, occlusions, and restricted camera angles. This research presents a human activity recognition system to address these challenges by working with drones’ red-green-blue (RGB) videos. The first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while… More >

  • Open Access

    REVIEW

    A Review on Vision-Language-Based Approaches: Challenges and Applications

    Huu-Tuong Ho, Luong Vuong Nguyen, Minh-Tien Pham, Quang-Huy Pham, Quang-Duong Tran, Duong Nguyen Minh Huy, Tri-Hai Nguyen
    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1733-1756, 2025, DOI:10.32604/cmc.2025.060363
    (This article belongs to the Special Issue: New Trends in Image Processing)
    Abstract In multimodal learning, Vision-Language Models (VLMs) have become a critical research focus, enabling the integration of textual and visual data. These models have shown significant promise across various natural language processing tasks, such as visual question answering and computer vision applications, including image captioning and image-text retrieval, highlighting their adaptability for complex, multimodal datasets. In this work, we review the landscape of Bootstrapping Language-Image Pre-training (BLIP) and other VLM techniques. A comparative analysis is conducted to assess VLMs’ strengths, limitations, and applicability across tasks while examining challenges such as scalability, data quality, and fine-tuning complexities. More >

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