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  • Open Access

    ARTICLE

    A Mathematical Model for COVID-19 Image Enhancement based on Mittag-Leffler-Chebyshev Shift

    Ibtisam Aldawish1, Hamid A. Jalab2,*

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1307-1316, 2022, DOI:10.32604/cmc.2022.029445

    Abstract The lungs CT scan is used to visualize the spread of the disease across the lungs to obtain better knowledge of the state of the COVID-19 infection. Accurately diagnosing of COVID-19 disease is a complex challenge that medical system face during the pandemic time. To address this problem, this paper proposes a COVID-19 image enhancement based on Mittag-Leffler-Chebyshev polynomial as pre-processing step for COVID-19 detection and segmentation. The proposed approach comprises the Mittag-Leffler sum convoluted with Chebyshev polynomial. The idea for using the proposed image enhancement model is that it improves images with low gray-level changes by estimating the probability… More >

  • Open Access

    ARTICLE

    Underwater Diver Image Enhancement via Dual-Guided Filtering

    Jingchun Zhou1,*, Taian Shi1, Weishi Zhang1,*, Weishen Chu2

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 1063-1081, 2022, DOI:10.32604/cmes.2022.019447

    Abstract The scattering and absorption of light propagating underwater cause the underwater images to present low contrast, color deviation, and loss of details, which in turn make human posture recognition challenging. To address these issues, this study introduced the dual-guided filtering technique and developed an underwater diver image improvement method. First, the color distortion of the underwater diver image was solved using white balance technology to obtain a color-corrected image. Second, dual-guided filtering was applied to the white balanced image to correct the distorted color and enhance its details. Four feature weight maps of the two images were then calculated, and… More >

  • Open Access

    ARTICLE

    Enhancement of Biomass Material Characterization Images Using an Improved U-Net

    Zuozheng Lian1, Hong Zhao2,*, Qianjun Zhang1, Haizhen Wang1, E. Erdun3

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1515-1528, 2022, DOI:10.32604/cmc.2022.024779

    Abstract For scanning electron microscopes with high resolution and a strong electric field, biomass materials under observation are prone to radiation damage from the electron beam. This results in blurred or non-viable images, which affect further observation of material microscopic morphology and characterization. Restoring blurred images to their original sharpness is still a challenging problem in image processing. Traditional methods can't effectively separate image context dependency and texture information, affect the effect of image enhancement and deblurring, and are prone to gradient disappearance during model training, resulting in great difficulty in model training. In this paper, we propose the use of… More >

  • Open Access

    ARTICLE

    Classification COVID-19 Based on Enhancement X-Ray Images and Low Complexity Model

    Aymen Saad1, Israa S. Kamil2, Ahmed Alsayat3, Ahmed Elaraby4,*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 561-576, 2022, DOI:10.32604/cmc.2022.023878

    Abstract COVID-19 has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world widespread. This spread of COVID-19 requires a fast technique for diagnosis to make the appropriate decision for the treatment. X-ray images are one of the most classifiable images that are used widely in diagnosing patients’ data depending on radiographs due to their structures and tissues that could be classified. Convolutional Neural Networks (CNN) is the most accurate classification technique used to diagnose COVID-19 because of the ability to use a different number of convolutional layers and its… More >

  • Open Access

    ARTICLE

    Mathematical Design Enhancing Medical Images Formulated by a Fractal Flame Operator

    Rabha W. Ibrahim1,*, Husam Yahya2, Arkan J. Mohammed3, Nadia M. G. Al-Saidi4, Dumitru Baleanu5,6,7

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 937-950, 2022, DOI:10.32604/iasc.2022.021954

    Abstract The interest in using fractal theory and its applications has grown in the field of image processing. Image enhancement is one of the feature processing tools, which aims to improve the details of an image. The enhancement of digital pictures is a challenging task due to the unforeseeable variation in the quality of the captured images. In this study, we present a mathematical model using a local conformable differential operator (LCDO). The proposed model is formulated by the theory of cantor fractal to generalize the definition of LCDO. The main advantage of utilizing LCDO for image enhancement is its capability… More >

  • Open Access

    ARTICLE

    A New Medical Image Enhancement Algorithm Based on Fractional Calculus

    Hamid A. Jalab1,*, Rabha W. Ibrahim2, Ali M. Hasan3, Faten Khalid Karim4, Ala’a R. Al-Shamasneh1, Dumitru Baleanu5,6,7

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1467-1483, 2021, DOI:10.32604/cmc.2021.016047

    Abstract The enhancement of medical images is a challenging research task due to the unforeseeable variation in the quality of the captured images. The captured images may present with low contrast and low visibility, which might influence the accuracy of the diagnosis process. To overcome this problem, this paper presents a new fractional integral entropy (FITE) that estimates the unforeseeable probabilities of image pixels, posing as the main contribution of the paper. The proposed model dynamically enhances the image based on the image contents. The main advantage of FITE lies in its capability to enhance the low contrast intensities through pixels’… More >

  • Open Access

    ARTICLE

    Fractional Rényi Entropy Image Enhancement for Deep Segmentation of Kidney MRI

    Hamid A. Jalab1, Ala’a R. Al-Shamasneh1, Hadil Shaiba2, Rabha W. Ibrahim3,4,*, Dumitru Baleanu5,6,7

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2061-2075, 2021, DOI:10.32604/cmc.2021.015170

    Abstract Recently, many rapid developments in digital medical imaging have made further contributions to health care systems. The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses. Many factors like image contrast and quality affect the result of image segmentation. Due to that, image contrast remains a challenging problem for image segmentation. This study presents a new image enhancement model based on fractional Rényi entropy for the segmentation of kidney MRI scans. The proposed work consists of two stages: enhancement by fractional Rényi entropy, and MRI Kidney deep segmentation. The proposed… More >

  • Open Access

    ARTICLE

    Recognition and Classification of Pomegranate Leaves Diseases by Image Processing and Machine Learning Techniques

    Mangena Venu Madhavan1, Dang Ngoc Hoang Thanh2, Aditya Khamparia1,*, Sagar Pande1, Rahul Malik1, Deepak Gupta3

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2939-2955, 2021, DOI:10.32604/cmc.2021.012466

    Abstract Disease recognition in plants is one of the essential problems in agricultural image processing. This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly. The framework utilizes image processing techniques such as image acquisition, image resizing, image enhancement, image segmentation, ROI extraction (region of interest), and feature extraction. An image dataset related to pomegranate leaf disease is utilized to implement the framework, divided into a training set and a test set. In the implementation process, techniques such as image enhancement and image segmentation are primarily used for identifying ROI and features. An image… More >

  • Open Access

    ARTICLE

    The Implementation of Optimization Methods for Contrast Enhancement

    Ahmet Elbir1,∗, Hamza Osman Ilhan1, Nizamettin Aydin1

    Computer Systems Science and Engineering, Vol.34, No.2, pp. 101-107, 2019, DOI:10.32604/csse.2019.34.101

    Abstract The performances of the multivariate techniques are directly related to the variable selection process, which is time consuming and requires resources for testing each possible parameter to achieve the best results. Therefore, optimization methods for variable selection process have been proposed in the literature to find the optimal solution in short time by using less system resources. Contrast enhancement is the one of the most important and the parameter dependent image enhancement technique. In this study, two optimization methods are employed for the variable selection for the contrast enhancement technique. Particle swarm optimization (PSO) and artificial bee colony (ABC) optimization… More >

  • Open Access

    ARTICLE

    Adaptive Image Enhancement Using Hybrid Particle Swarm Optimization and Watershed Segmentation

    N. Mohanapriya1, Dr. B. Kalaavathi2

    Intelligent Automation & Soft Computing, Vol.25, No.4, pp. 663-672, 2019, DOI:10.31209/2018.100000041

    Abstract Medical images are obtained straight from the medical acquisition devices so that, the image quality becomes poor and may contain noises. Low contrast and poor quality are the major issues in the production of medical images. Medical imaging enhancement technology gives way to solve these issues; it helps the doctors to see the interior portions of the body for early diagnosis, also it improves the features the visual aspects of an image for a right diagnosis. This paper proposes a new blend of Particle Swarm Optimization (PSO) and Accelerated Particle Swarm Optimization (APSO) called Hybrid Partial Swarm Optimization (HPSO) to… More >

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