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

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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

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

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

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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ARTICLE

Explainable Diabetic Retinopathy Detection Using a Distributed CNN and LightGBM Framework
Pooja Bidwai, Shilpa Gite, Biswajeet Pradhan, Abdullah Almari
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061018
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract Diabetic Retinopathy (DR) is a critical disorder that affects the retina due to the constant rise in diabetics and remains the major cause of blindness across the world. Early detection and timely treatment are essential to mitigate the effects of DR, such as retinal damage and vision impairment. Several conventional approaches have been proposed to detect DR early and accurately, but they are limited by data imbalance, interpretability, overfitting, convergence time, and other issues. To address these drawbacks and improve DR detection accurately, a distributed Explainable Convolutional Neural network-enabled Light Gradient Boosting Machine (DE-ExLNN) is… More >

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ARTICLE

SFC_DeepLabv3+: A Lightweight Grape Image Segmentation Method Based on Content-Guided Attention Fusion
Yuchao Xia, Jing Qiu
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.064635
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract In recent years, fungal diseases affecting grape crops have attracted significant attention. Currently, the assessment of black rot severity mainly depends on the ratio of lesion area to leaf surface area. However, effectively and accurately segmenting leaf lesions presents considerable challenges. Existing grape leaf lesion segmentation models have several limitations, such as a large number of parameters, long training durations, and limited precision in extracting small lesions and boundary details. To address these issues, we propose an enhanced DeepLabv3+ model incorporating Strip Pooling, Content-Guided Fusion, and Convolutional Block Attention Module (SFC_DeepLabv3+), an enhanced lesion segmentation method based… More >

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ARTICLE

Bird Species Classification Using Image Background Removal for Data Augmentation
Yu-Xiang Zhao, Yi Lee
CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 791-810, 2025, DOI:10.32604/cmc.2025.065048
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract Bird species classification is not only a challenging topic in artificial intelligence but also a domain closely related to environmental protection and ecological research. Additionally, performing edge computing on low-level devices using small neural networks can be an important research direction. In this paper, we use the EfficientNetV2B0 model for bird species classification, applying transfer learning on a dataset of 525 bird species. We also employ the BiRefNet model to remove backgrounds from images in the training set. The generated background-removed images are mixed with the original training set as a form of data augmentation.… More >

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ARTICLE

Efficient Method for Trademark Image Retrieval: Leveraging Siamese and Triplet Networks with Examination-Informed Loss Adjustment
Thanh Bui-Minh, Nguyen Long Giang, Luan Thanh Le
CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1203-1226, 2025, DOI:10.32604/cmc.2025.064403
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract Image-based similar trademark retrieval is a time-consuming and labor-intensive task in the trademark examination process. This paper aims to support trademark examiners by training Deep Convolutional Neural Network (DCNN) models for effective Trademark Image Retrieval (TIR). To achieve this goal, we first develop a novel labeling method that automatically generates hundreds of thousands of labeled similar and dissimilar trademark image pairs using accompanying data fields such as citation lists, Vienna classification (VC) codes, and trademark ownership information. This approach eliminates the need for manual labeling and provides a large-scale dataset suitable for training deep learning… More >

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ARTICLE

DNEFNET: Denoising and Frequency Domain Feature Enhancement Event Fusion Network for Image Deblurring
Kangkang Zhao, Yaojie Chen, Jianbo Li
CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 745-762, 2025, DOI:10.32604/cmc.2025.063906
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract Traditional cameras inevitably suffer from motion blur when facing high-speed moving objects. Event cameras, as high temporal resolution bionic cameras, record intensity changes in an asynchronous manner, and their recorded high temporal resolution information can effectively solve the problem of time information loss in motion blur. Existing event-based deblurring methods still face challenges when facing high-speed moving objects. We conducted an in-depth study of the imaging principle of event cameras. We found that the event stream contains excessive noise. The valid information is sparse. Invalid event features hinder the expression of valid features due to… More >

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ARTICLE

An Improved Lightweight Safety Helmet Detection Algorithm for YOLOv8
Lieping Zhang, Hao Ma, Jiancheng Huang, Cui Zhang, Xiaolin Gao
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2245-2265, 2025, 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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ARTICLE

Multimodal Gas Detection Using E-Nose and Thermal Images: An Approach Utilizing SRGAN and Sparse Autoencoder
Pratik Jadhav, Vuppala Adithya Sairam, Niranjan Bhojane, Abhyuday Singh, Shilpa Gite, Biswajeet Pradhan, Mrinal Bachute, Abdullah Alamri
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3493-3517, 2025, DOI:10.32604/cmc.2025.060764
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract Electronic nose and thermal images are effective ways to diagnose the presence of gases in real-time real-time. Multimodal fusion of these modalities can result in the development of highly accurate diagnostic systems. The low-cost thermal imaging software produces low-resolution thermal images in grayscale format, hence necessitating methods for improving the resolution and colorizing the images. The objective of this paper is to develop and train a super-resolution generative adversarial network for improving the resolution of the thermal images, followed by a sparse autoencoder for colorization of thermal images and a multimodal convolutional neural network for… More >

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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, Vol.83, No.2, pp. 2865-2888, 2025, 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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ARTICLE

Token Masked Pose Transformers Are Efficient Learners
Xinyi Song, Haixiang Zhang, Shaohua Li
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2735-2750, 2025, 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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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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226
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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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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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279
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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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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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Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition

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714

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714

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