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Search Results (12)
  • Open Access


    Variant Wasserstein Generative Adversarial Network Applied on Low Dose CT Image Denoising

    Anoud A. Mahmoud1,*, Hanaa A. Sayed2,3, Sara S. Mohamed1

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4535-4552, 2023, DOI:10.32604/cmc.2023.037087

    Abstract Computed Tomography (CT) images have been extensively employed in disease diagnosis and treatment, causing a huge concern over the dose of radiation to which patients are exposed. Increasing the radiation dose to get a better image may lead to the development of genetic disorders and cancer in the patients; on the other hand, decreasing it by using a Low-Dose CT (LDCT) image may cause more noise and increased artifacts, which can compromise the diagnosis. So, image reconstruction from LDCT image data is necessary to improve radiologists’ judgment and confidence. This study proposed three novel models for denoising LDCT images based… More >

  • Open Access


    Adaptive Noise Detector and Partition Filter for Image Restoration

    Cong Lin1, Chenghao Qiu1, Can Wu1, Siling Feng1,*, Mengxing Huang1,2,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4317-4340, 2023, DOI:10.32604/cmc.2023.036249

    Abstract The random-value impulse noise (RVIN) detection approach in image denoising, which is dependent on manually defined detection thresholds or local window information, does not have strong generalization performance and cannot successfully cope with damaged pictures with high noise levels. The fusion of the K-means clustering approach in the noise detection stage is reviewed in this research, and the internal relationship between the flat region and the detail area of the damaged picture is thoroughly explored to suggest an unique two-stage method for gray image denoising. Based on the concept of pixel clustering and grouping, all pixels in the damaged picture… More >

  • Open Access


    Novel Double Modular Redundancy Based Fault-Tolerant FIR Filter for Image Denoising

    V. S. Vaisakhi1,*, D. Surendran2

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 181-193, 2023, DOI:10.32604/csse.2023.032514

    Abstract In signal processing and communication systems, digital filters are widely employed. In some circumstances, the reliability of those systems is crucial, necessitating the use of fault tolerant filter implementations. Many strategies have been presented throughout the years to achieve fault tolerance by utilising the structure and properties of the filters. As technology advances, more complicated systems with several filters become possible. Some of the filters in those complicated systems frequently function in parallel, for example, by applying the same filter to various input signals. Recently, a simple strategy for achieving fault tolerance that takes advantage of the availability of parallel… More >

  • Open Access


    Deep CNN Model for Multimodal Medical Image Denoising

    Walid El-Shafai1,2, Amira A. Mahmoud1, Anas M. Ali1,3, El-Sayed M. El-Rabaie1, Taha E. Taha1, Osama F. Zahran1, Adel S. El-Fishawy1, Naglaa F. Soliman4, Amel A. Alhussan5,*, Fathi E. Abd El-Samie1

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3795-3814, 2022, DOI:10.32604/cmc.2022.029134

    Abstract In the literature, numerous techniques have been employed to decrease noise in medical image modalities, including X-Ray (XR), Ultrasonic (Us), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). These techniques are organized into two main classes: the Multiple Image (MI) and the Single Image (SI) techniques. In the MI techniques, images usually obtained for the same area scanned from different points of view are used. A single image is used in the entire procedure in the SI techniques. SI denoising techniques can be carried out both in a transform or spatial domain. This paper is concerned… More >

  • Open Access


    An FPGA Design for Real-Time Image Denoising

    Ahmed Ben Atitallah*

    Computer Systems Science and Engineering, Vol.43, No.2, pp. 803-816, 2022, DOI:10.32604/csse.2022.024393

    Abstract The increasing use of images in miscellaneous applications such as medical image analysis and visual quality inspection has led to growing interest in image processing. However, images are often contaminated with noise which may corrupt any of the following image processing steps. Therefore, noise filtering is often a necessary preprocessing step for the most image processing applications. Thus, in this paper an optimized field-programmable gate array (FPGA) design is proposed to implement the adaptive vector directional distance filter (AVDDF) in hardware/software (HW/SW) codesign context for removing noise from the images in real-time. For that, the high-level synthesis (HLS) flow is… More >

  • Open Access


    Image Denoising Using a Nonlinear Pixel-Likeness Weighted-Frame Technique

    P. Vinayagam1,*, P. Anandan2, N. Kumaratharan3

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 869-879, 2021, DOI:10.32604/iasc.2021.016761

    Abstract Recent advances in the development of image denoising applications for eliminating the various sources of noise in digital images have employed hardware platforms based on field programmable gate arrays for attaining speed and efficiency, which are essential factors in real-time applications. However, image denoising providing for maximum denoising performance, speed, and efficiency on these platforms is subject to constant innovation. To this end, the present work proposes a high-throughput fixed-point adaptive edge noise filter architecture to denoise digital images with additive white Gaussian noise in realtime using a nonlinear modified pixel-likeness weighted-frame technique. The proposed architecture works in two stages.… More >

  • Open Access


    Guided Intra-Patch Smoothing Graph Filtering for Single-Image Denoising

    Yibin Tang1, Ying Chen2, Aimin Jiang1, Jian Li1, Yan Zhou1,*, Hon Keung Kwan3

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 67-80, 2021, DOI:10.32604/cmc.2021.017300

    Abstract Graph filtering is an important part of graph signal processing and a useful tool for image denoising. Existing graph filtering methods, such as adaptive weighted graph filtering (AWGF), focus on coefficient shrinkage strategies in a graph-frequency domain. However, they seldom consider the image attributes in their graph-filtering procedure. Consequently, the denoising performance of graph filtering is barely comparable with that of other state-of-the-art denoising methods. To fully exploit the image attributes, we propose a guided intra-patch smoothing AWGF (AWGF-GPS) method for single-image denoising. Unlike AWGF, which employs graph topology on patches, AWGF-GPS learns the topology of superpixels by introducing the… More >

  • Open Access


    Image Denoising with GAN Based Model

    Peizhu Gong, Jin Liu*, Shiqi Lv

    Journal of Information Hiding and Privacy Protection, Vol.2, No.4, pp. 155-163, 2020, DOI:10.32604/jihpp.2020.010453

    Abstract Image denoising is often used as a preprocessing step in computer vision tasks, which can help improve the accuracy of image processing models. Due to the imperfection of imaging systems, transmission media and recording equipment, digital images are often contaminated with various noises during their formation, which troubles the visual effects and even hinders people’s normal recognition. The pollution of noise directly affects the processing of image edge detection, feature extraction, pattern recognition, etc., making it difficult for people to break through the bottleneck by modifying the model. Many traditional filtering methods have shown poor performance since they do not… More >

  • Open Access


    Image Denoising Based on the Asymmetric Gaussian Mixture Model

    Ke Jin, Shunfeng Wang*

    Journal on Internet of Things, Vol.2, No.1, pp. 1-11, 2020, DOI:10.32604/jiot.2020.09071

    Abstract In recent years, image restoration has become a huge subject, and finite hybrid model has been widely used in image denoising because of its easy modeling and strong explanatory results. The gaussian mixture model is the most common one. The existing image denoising methods usually assume that each component of the natural image is subject to the gaussian mixture model (GMM). However, this approach is not entirely reasonable. It is well known that most natural images are complex and their distribution is not entirely gaussian. As a result, there are still many problems that GMM cannot solve. This paper tries… More >

  • Open Access


    A New Adaptive Regularization Parameter Selection Based on Expected Patch Log Likelihood

    Jianwei Zhang1, Ze Qin1, Shunfeng Wang1, *

    Journal of Cyber Security, Vol.2, No.1, pp. 25-36, 2020, DOI:10.32604/jcs.2020.06429

    Abstract Digital images have been applied to various areas such as evidence in courts. However, it always suffers from noise by criminals. This type of computer network security has become a hot issue that can’t be ignored. In this paper, we focus on noise removal so as to provide guarantees for computer network security. Firstly, we introduce a well-known denoising method called Expected Patch Log Likelihood (EPLL) with Gaussian Mixture Model as its prior. This method achieves exciting results in noise removal. However, there remain problems to be solved such as preserving the edge and meaningful details in image denoising, cause… More >

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