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Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition

Submission Deadline: 31 May 2025 View: 1117 Submit to Special Issue

Guest Editors

Prof. Dr. Biswajeet Pradhan

Email: Biswajeet.Pradhan@uts.edu.au

Affiliation: Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering & IT, University of Technology Sydney, Sydney, Australia

Homepage:

Research Interests: Geospatial Information Systems (GIS), remote sensing and image processing, complex modeling/geo-computing, machine learning, soft-computing applications, natural hazards and environmental modeling, remote sensing of Earth observation

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Prof. Dr. Shilpa Bade-Gite

Email: shilpa.gite@sitpune.edu.in

Affiliation: Artificial Intelligence and Machine Learning Department, Symbiosis Institute of Technology, Symbiosis Centre for Applied AI, Symbiosis International (Deemed University), Pune, India

Homepage:

Research Interests: deep learning, computer vision, multi-sensor data fusion, assistive driving

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Summary

With advances in Artificial Intelligence, the fields of image processing and computer vision have significantly impacted our daily lives. Computer vision strives to enable computers to interpret and understand visual information as humans do, while image processing often serves as a precursor to more advanced computer vision tasks. Key areas include object detection and recognition, image segmentation, video analytics, facial recognition, activity recognition, scene understanding, and more.

 

This special issue on “Computer Vision and Image Processing: Feature Selection, Image Enhancement, and Recognition” invites researchers to submit original research articles that explore cutting-edge advancements and applications in areas such as autonomous vehicles, remote sensing, earth observation, medical imaging, video surveillance and security, augmented reality (AR) and virtual reality (VR), and vision-based quality control.

 

Topics of interest include, but are not limited to:

 

Advanced image processing techniques

Intelligent image analysis

Vision-based intelligent systems

Image recognition and classification

Medical imaging and health informatics

Machine learning in data and image processing

Smart environments and smart cities

Deep learning for object detection

Image augmentation techniques

Real-time object tracking

Drone imaging

Remote surveillance

Satellite imaging

Advanced feature selection and processing techniques

Advanced image enhancement

Generative adversarial networks (GANs)

Stable diffusion models of image generation

2D/3D object recognition

Quality control using vision sensors

 

We welcome submissions that contribute to the theoretical foundations, methodologies, and practical applications in these areas. Both research articles and extensive review articles are allowed.

Keywords

image processing techniques, image enhancement, feature selection and processing, vision-based intelligent systems, 2D/3D object recognition, medical imaging, Satellite imaging, generative adversarial networks, diffusion models, image generation

Published Papers

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  • Open Access

    ARTICLE

    Token Masked Pose Transformers Are Efficient Learners

    Xinyi Song, Haixiang Zhang, Shaohua Li
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.059006
    （This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
    Abstract In recent years, Transformer has achieved remarkable results in the field of computer vision, with its built-in attention layers effectively modeling global dependencies in images by transforming image features into token forms. However, Transformers often face high computational costs when processing large-scale image data, which limits their feasibility in real-time applications. To address this issue, we propose Token Masked Pose Transformers (TMPose), constructing an efficient Transformer network for pose estimation. This network applies semantic-level masking to tokens and employs three different masking strategies to optimize model performance, aiming to reduce computational complexity. Experimental results show More >

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    139

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    50

  • Open Access

    ARTICLE

    A Deep Learning-Based Salient Feature-Preserving Algorithm for Mesh Simplification

    Jiming Lan, Bo Zeng, Suiqun Li, Weihan Zhang, Xinyi Shi
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060260
    （This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
    Abstract The Quadric Error Metrics (QEM) algorithm is a widely used method for mesh simplification; however, it often struggles to preserve high-frequency geometric details, leading to the loss of salient features. To address this limitation, we propose the Salient Feature Sampling Points-based QEM (SFSP-QEM)—also referred to as the Deep Learning-Based Salient Feature-Preserving Algorithm for Mesh Simplification—which incorporates a Salient Feature-Preserving Point Sampler (SFSP). This module leverages deep learning techniques to prioritize the preservation of key geometric features during simplification. Experimental results demonstrate that SFSP-QEM significantly outperforms traditional QEM in preserving geometric details. Specifically, for general models… More >

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    234

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    72

  • Open Access

    ARTICLE

    An Improved Lightweight Safety Helmet Detection Algorithm for YOLOv8

    Lieping Zhang, Hao Ma, Jiancheng Huang, Cui Zhang, Xiaolin Gao
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061519
    （This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
    Abstract Detecting individuals wearing safety helmets in complex environments faces several challenges. These factors include limited detection accuracy and frequent missed or false detections. Additionally, existing algorithms often have excessive parameter counts, complex network structures, and high computational demands. These challenges make it difficult to deploy such models efficiently on resource-constrained devices like embedded systems. Aiming at this problem, this research proposes an optimized and lightweight solution called FGP-YOLOv8, an improved version of YOLOv8n. The YOLOv8 backbone network is replaced with the FasterNet model to reduce parameters and computational demands while local convolution layers are added.… More >

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    257

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    97

  • Open Access

    ARTICLE

    Image Super-Resolution Reconstruction Based on the DSSTU-Net Model

    Bonan Yu, Taiping Mo, Qi Ma, Qiumei Li, Peng Sun
    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1057-1078, 2025, DOI:10.32604/cmc.2025.059946
    （This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
    Abstract Super-resolution (SR) reconstruction addresses the challenge of enhancing image resolution, which is critical in domains such as medical imaging, remote sensing, and computational photography. High-quality image reconstruction is essential for enhancing visual details and improving the accuracy of subsequent tasks. Traditional methods, including interpolation techniques and basic CNNs, often fail to recover fine textures and detailed structures, particularly in complex or high-frequency regions. In this paper, we present Deep Supervised Swin Transformer U-Net (DSSTU-Net), a novel architecture designed to improve image SR by integrating Residual Swin Transformer Blocks (RSTB) and Deep Supervision (DS) mechanisms into… More >

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    228

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    93

  • Open Access

    ARTICLE

    An Uncertainty Quantization-Based Method for Anti-UAV Detection in Infrared Images

    Can Wu, Wenyi Tang, Yunbo Rao, Yinjie Chen, Hui Ding, Shuzhen Zhu, Yuanyuan Wang
    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1415-1434, 2025, DOI:10.32604/cmc.2025.059797
    （This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
    Abstract Infrared unmanned aerial vehicle (UAV) target detection presents significant challenges due to the interplay between small targets and complex backgrounds. Traditional methods, while effective in controlled environments, often fail in scenarios involving long-range targets, high noise levels, or intricate backgrounds, highlighting the need for more robust approaches. To address these challenges, we propose a novel three-stage UAV segmentation framework that leverages uncertainty quantification to enhance target saliency. This framework incorporates a Bayesian convolutional neural network capable of generating both segmentation maps and probabilistic uncertainty maps. By utilizing uncertainty predictions, our method refines segmentation outcomes, achieving… More >

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    284

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    86

  • Open Access

    ARTICLE

    Blur-Deblur Algorithm for Pressure-Sensitive Paint Image Based on Variable Attention Convolution

    Ruizhe Yu, Tingrui Yue, Lei Liang, Zhisheng Gao
    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5239-5256, 2025, DOI:10.32604/cmc.2025.059077
    （This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
    Abstract In the PSP (Pressure-Sensitive Paint), image deblurring is essential due to factors such as prolonged camera exposure times and high model velocities, which can lead to significant image blurring. Conventional deblurring methods applied to PSP images often suffer from limited accuracy and require extensive computational resources. To address these issues, this study proposes a deep learning-based approach tailored for PSP image deblurring. Considering that PSP applications primarily involve the accurate pressure measurements of complex geometries, the images captured under such conditions exhibit distinctive non-uniform motion blur, presenting challenges for standard deep learning models utilizing convolutional… More >

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    275

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    168

  • Open Access

    ARTICLE

    Unsupervised Low-Light Image Enhancement Based on Explicit Denoising and Knowledge Distillation

    Wenkai Zhang, Hao Zhang, Xianming Liu, Xiaoyu Guo, Xinzhe Wang, Shuiwang Li
    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2537-2554, 2025, DOI:10.32604/cmc.2024.059000
    （This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
    Abstract Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images. Supervised methods, which utilize paired high-low light images as training sets, can enhance the PSNR to around 20 dB, significantly improving image quality. However, such data is challenging to obtain. In recent years, unsupervised low-light image enhancement (LIE) methods based on the Retinex framework have been proposed, but they generally lag behind supervised methods by 5–10 dB in performance. In this paper, we introduce the Denoising-Distilled… More >

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    551

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    246

  • Open Access

    ARTICLE

    CSRWA: Covert and Severe Attacks Resistant Watermarking Algorithm

    Balsam Dhyia Majeed, Amir Hossein Taherinia, Hadi Sadoghi Yazdi, Ahad Harati
    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1027-1047, 2025, DOI:10.32604/cmc.2024.059789
    （This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
    Abstract Watermarking is embedding visible or invisible data within media to verify its authenticity or protect copyright. The watermark is embedded in significant spatial or frequency features of the media to make it more resistant to intentional or unintentional modification. Some of these features are important perceptual features according to the human visual system (HVS), which means that the embedded watermark should be imperceptible in these features. Therefore, both the designers of watermarking algorithms and potential attackers must consider these perceptual features when carrying out their actions. The two roles will be considered in this paper… More >

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    660

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    489

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