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  • Open Access


    Shear Let Transform Residual Learning Approach for Single-Image Super-Resolution

    Israa Ismail1,*, Ghada Eltaweel1, Mohamed Meselhy Eltoukhy1,2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3193-3209, 2024, DOI:10.32604/cmc.2023.043873

    Abstract Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution inputs. Super-resolution is of paramount importance in the context of remote sensing, satellite, aerial, security and surveillance imaging. Super-resolution remote sensing imagery is essential for surveillance and security purposes, enabling authorities to monitor remote or sensitive areas with greater clarity. This study introduces a single-image super-resolution approach for remote sensing images, utilizing deep shearlet residual learning in the shearlet transform domain, and incorporating the Enhanced Deep Super-Resolution network (EDSR). Unlike conventional approaches that estimate residuals between high and… More >

  • Open Access


    Robust Watermarking Algorithm for Medical Images Based on Non-Subsampled Shearlet Transform and Schur Decomposition

    Meng Yang1, Jingbing Li1,2,*, Uzair Aslam Bhatti1,2, Chunyan Shao1, Yen-Wei Chen3

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5539-5554, 2023, DOI:10.32604/cmc.2023.036904

    Abstract With the development of digitalization in healthcare, more and more information is delivered and stored in digital form, facilitating people’s lives significantly. In the meanwhile, privacy leakage and security issues come along with it. Zero watermarking can solve this problem well. To protect the security of medical information and improve the algorithm’s robustness, this paper proposes a robust watermarking algorithm for medical images based on Non-Subsampled Shearlet Transform (NSST) and Schur decomposition. Firstly, the low-frequency subband image of the original medical image is obtained by NSST and chunked. Secondly, the Schur decomposition of low-frequency blocks… More >

  • Open Access


    Skin Lesion Classification System Using Shearlets

    S. Mohan Kumar*, T. Kumanan

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 833-844, 2023, DOI:10.32604/csse.2023.022385

    Abstract The main cause of skin cancer is the ultraviolet radiation of the sun. It spreads quickly to other body parts. Thus, early diagnosis is required to decrease the mortality rate due to skin cancer. In this study, an automatic system for Skin Lesion Classification (SLC) using Non-Subsampled Shearlet Transform (NSST) based energy features and Support Vector Machine (SVM) classifier is proposed. At first, the NSST is used for the decomposition of input skin lesion images with different directions like 2, 4, 8 and 16. From the NSST’s sub-bands, energy features are extracted and stored in More >

  • Open Access


    Secured Medical Data Transfer Using Reverse Data Hiding System Through Steganography

    S. Aiswarya*, R. Gomathi

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 969-982, 2022, DOI:10.32604/iasc.2022.025475

    Abstract Reversible Data Hiding (RDH) is the process of transferring secret data hidden inside cover media to the recipient so the recipient can securely retrieve both the secret data and cover media. The RDH approach is applied in this study in the field of telemedicine, and medical-secret data is conveyed privately via medical cover video. Morse code-based data encryption technique tends to encrypt the medical-secret data by compression using the Arithmetic coding technique. Discrete Shearlet transform (DST) compresses the selected frame from the medical cover video and the compressed secret data is embedded into the compressed… More >

  • Open Access


    Remote Sensing Image Retrieval Based on 3D-Local Ternary Pattern (LTP) Features and Non-subsampled Shearlet Transform (NSST) Domain Statistical Features

    Hilly Gohain Baruah*, Vijay Kumar Nath, Deepika Hazarika

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 137-164, 2022, DOI:10.32604/cmes.2022.018339

    Abstract With the increasing popularity of high-resolution remote sensing images, the remote sensing image retrieval (RSIR) has always been a topic of major issue. A combined, global non-subsampled shearlet transform (NSST)-domain statistical features (NSSTds) and local three dimensional local ternary pattern (3D-LTP) features, is proposed for high-resolution remote sensing images. We model the NSST image coefficients of detail subbands using 2-state laplacian mixture (LM) distribution and its three parameters are estimated using Expectation-Maximization (EM) algorithm. We also calculate the statistical parameters such as subband kurtosis and skewness from detail subbands along with mean and standard deviation… More >

  • Open Access


    An Automated Brain Image Analysis System for Brain Cancer using Shearlets

    R. Muthaiyan1,*, Dr M. Malleswaran2

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 299-312, 2022, DOI:10.32604/csse.2022.018034

    Abstract In this paper, an Automated Brain Image Analysis (ABIA) system that classifies the Magnetic Resonance Imaging (MRI) of human brain is presented. The classification of MRI images into normal or low grade or high grade plays a vital role for the early diagnosis. The Non-Subsampled Shearlet Transform (NSST) that captures more visual information than conventional wavelet transforms is employed for feature extraction. As the feature space of NSST is very high, a statistical t-test is applied to select the dominant directional sub-bands at each level of NSST decomposition based on sub-band energies. A combination of… More >

  • Open Access


    Infrared and Visible Image Fusion Based on NSST and RDN

    Peizhou Yan1, Jiancheng Zou2,*, Zhengzheng Li1, Xin Yang3

    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 213-225, 2021, DOI:10.32604/iasc.2021.016201

    Abstract Within the application of driving assistance systems, the detection of driver’s facial features in the cab for a spectrum of luminosities is mission critical. One method that addresses this concern is infrared and visible image fusion. Its purpose is to generate an aggregate image which can granularly and systematically illustrate scene details in a range of lighting conditions. Our study introduces a novel approach to this method with marked improvements. We utilize non-subsampled shearlet transform (NSST) to obtain the low and high frequency sub-bands of infrared and visible imagery. For the low frequency sub-band fusion,… More >

  • Open Access


    A Hybrid Artificial Intelligence Model for Skin Cancer Diagnosis

    V. Vidya Lakshmi1,*, J. S. Leena Jasmine2

    Computer Systems Science and Engineering, Vol.37, No.2, pp. 233-245, 2021, DOI:10.32604/csse.2021.015700

    Abstract Melanoma or skin cancer is the most dangerous and deadliest disease. As the incidence and mortality rate of skin cancer increases worldwide, an automated skin cancer detection/classification system is required for early detection and prevention of skin cancer. In this study, a Hybrid Artificial Intelligence Model (HAIM) is designed for skin cancer classification. It uses diverse multi-directional representation systems for feature extraction and an efficient Exponentially Weighted and Heaped Multi-Layer Perceptron (EWHMLP) for the classification. Though the wavelet transform is a powerful tool for signal and image processing, it is unable to detect the intermediate More >

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