Special Issues
Table of Content

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

Submission Deadline: 31 May 2025 View: 534 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


  • 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, 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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