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DDT-Net: Deep Detail Tracking Network for Image Tampering Detection
Jim Wong, Zhaoxiang Zang
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3451-3469, 2025, DOI:10.32604/cmc.2025.061006
（This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)
Abstract In the field of image forensics, image tampering detection is a critical and challenging task. Traditional methods based on manually designed feature extraction typically focus on a specific type of tampering operation, which limits their effectiveness in complex scenarios involving multiple forms of tampering. Although deep learning-based methods offer the advantage of automatic feature learning, current approaches still require further improvements in terms of detection accuracy and computational efficiency. To address these challenges, this study applies the U-Net 3+ model to image tampering detection and proposes a hybrid framework, referred to as DDT-Net (Deep Detail… More >

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KD-SegNet: Efficient Semantic Segmentation Network with Knowledge Distillation Based on Monocular Camera
Thai-Viet Dang, Nhu-Nghia Bui, Phan Xuan Tan
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2001-2026, 2025, DOI:10.32604/cmc.2025.060605
（This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)
Abstract Due to the necessity for lightweight and efficient network models, deploying semantic segmentation models on mobile robots (MRs) is a formidable task. The fundamental limitation of the problem lies in the training performance, the ability to effectively exploit the dataset, and the ability to adapt to complex environments when deploying the model. By utilizing the knowledge distillation techniques, the article strives to overcome the above challenges with the inheritance of the advantages of both the teacher model and the student model. More precisely, the ResNet152-PSP-Net model’s characteristics are utilized to train the ResNet18-PSP-Net model. Pyramid… More >

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Novel Feature Extractor Framework in Conjunction with Supervised Three Class-XGBoost Algorithm for Osteosarcoma Detection from Whole Slide Medical Histopathology Images
Tanzila Saba, Muhammad Mujahid, Shaha Al-Otaibi, Noor Ayesha, Amjad Rehman Khan
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3337-3353, 2025, DOI:10.32604/cmc.2025.060163
（This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)
Abstract Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone; however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with… More >

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ARTICLE

Retinexformer+: Retinex-Based Dual-Channel Transformer for Low-Light Image Enhancement
Song Liu, Hongying Zhang, Xue Li, Xi Yang
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1969-1984, 2025, DOI:10.32604/cmc.2024.057662
（This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)
Abstract Enhancing low-light images with color distortion and uneven multi-light source distribution presents challenges. Most advanced methods for low-light image enhancement are based on the Retinex model using deep learning. Retinexformer introduces channel self-attention mechanisms in the IG-MSA. However, it fails to effectively capture long-range spatial dependencies, leaving room for improvement. Based on the Retinexformer deep learning framework, we designed the Retinexformer+ network. The “+” signifies our advancements in extracting long-range spatial dependencies. We introduced multi-scale dilated convolutions in illumination estimation to expand the receptive field. These convolutions effectively capture the weakening semantic dependency between pixels… More >

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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans
Nahed Tawfik, Heba M. Emara, Walid El-Shafai, Naglaa F. Soliman, Abeer D. Algarni, Fathi E. Abd El-Samie
CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 271-307, 2024, DOI:10.32604/cmc.2024.052404
（This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)
Abstract Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins, including hereditary factors and various clinical changes. It stands as the deadliest type of cancer and a significant cause of cancer-related deaths globally. Early diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately, leading to improved prognosis and higher survival rates. The significant increase in both the incidence and mortality rates of lung cancer, particularly its ranking as the second most prevalent cancer among women worldwide, underscores the need for comprehensive research into efficient… More >

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Pyramid Separable Channel Attention Network for Single Image Super-Resolution
Congcong Ma, Jiaqi Mi, Wanlin Gao, Sha Tao
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4687-4701, 2024, DOI:10.32604/cmc.2024.055803
（This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)
Abstract Single Image Super-Resolution (SISR) technology aims to reconstruct a clear, high-resolution image with more information from an input low-resolution image that is blurry and contains less information. This technology has significant research value and is widely used in fields such as medical imaging, satellite image processing, and security surveillance. Despite significant progress in existing research, challenges remain in reconstructing clear and complex texture details, with issues such as edge blurring and artifacts still present. The visual perception effect still needs further enhancement. Therefore, this study proposes a Pyramid Separable Channel Attention Network (PSCAN) for the… More >

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An Improved Image Steganography Security and Capacity Using Ant Colony Algorithm Optimization
Zinah Khalid Jasim Jasim, Sefer Kurnaz
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4643-4662, 2024, DOI:10.32604/cmc.2024.055195
（This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)
Abstract This advanced paper presents a new approach to improving image steganography using the Ant Colony Optimization (ACO) algorithm. Image steganography, a technique of embedding hidden information in digital photographs, should ideally achieve the dual purposes of maximum data hiding and maintenance of the integrity of the cover media so that it is least suspect. The contemporary methods of steganography are at best a compromise between these two. In this paper, we present our approach, entitled Ant Colony Optimization (ACO)-Least Significant Bit (LSB), which attempts to optimize the capacity in steganographic embedding. The approach makes use… More >

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Improving the Effectiveness of Image Classification Structural Methods by Compressing the Description According to the Information Content Criterion
Yousef Ibrahim Daradkeh, Volodymyr Gorokhovatskyi, Iryna Tvoroshenko, Medien Zeghid
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3085-3106, 2024, DOI:10.32604/cmc.2024.051709
（This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)
Abstract The research aims to improve the performance of image recognition methods based on a description in the form of a set of keypoint descriptors. The main focus is on increasing the speed of establishing the relevance of object and etalon descriptions while maintaining the required level of classification efficiency. The class to be recognized is represented by an infinite set of images obtained from the etalon by applying arbitrary geometric transformations. It is proposed to reduce the descriptions for the etalon database by selecting the most significant descriptor components according to the information content criterion.… More >

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Optimized Binary Neural Networks for Road Anomaly Detection: A TinyML Approach on Edge Devices
Amna Khatoon, Weixing Wang, Asad Ullah, Limin Li, Mengfei Wang
CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 527-546, 2024, DOI:10.32604/cmc.2024.051147
（This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)
Abstract Integrating Tiny Machine Learning (TinyML) with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level. Constrained devices efficiently implement a Binary Neural Network (BNN) for road feature extraction, utilizing quantization and compression through a pruning strategy. The modifications resulted in a 28-fold decrease in memory usage and a 25% enhancement in inference speed while only experiencing a 2.5% decrease in accuracy. It showcases its superiority over conventional detection algorithms in different road image scenarios. Although constrained by computer resources and training datasets, our results indicate opportunities for More >

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Data and Image Processing in Intelligent Information Systems

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