Home / Journals / JIOT / Vol.2, No.1, 2020
  • Open Access

    ARTICLE

    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

    ARTICLE

    An Expected Patch Log Likelihood Denoising Method Based on Internal and External Image Similarity

    Peng Xu, Jianwei Zhang*
    Journal on Internet of Things, Vol.2, No.1, pp. 13-21, 2020, DOI:10.32604/jiot.2020.09073
    Abstract Nonlocal property is an important feature of natural images, which means that the patch matrix formed by similar image patches is low-rank. Meanwhile, learning good image priors is of great importance for image denoising. In this paper, we combine the image self-similarity with EPLL (Expected patch log likelihood) method, and propose an EPLL denoising model based on internal and external image similarity to improve the preservation of image details. The experiment results show that the validity of our method is proved from two aspects of visual and numerical results. More >

  • Open Access

    ARTICLE

    Research on Indoor Passive Positioning Technology Based on WiFi

    Lei Sun1, Ling Tan1,*, Wenjie Ma1, Jingming Xia2
    Journal on Internet of Things, Vol.2, No.1, pp. 23-35, 2020, DOI:10.32604/jiot.2020.09075
    Abstract In recent years, WiFi indoor positioning technology has become a hot research topic at home and abroad. However, at present, indoor positioning technology still has many problems in terms of practicability and stability, which seriously affects the accuracy of indoor positioning and increases the complexity of the calculation process. Aiming at the instability of RSS and the more complicated data processing, this paper proposes a low-frequency filtering method based on fast data convergence. Low-frequency filtering uses MATLAB for data fitting to filter out low-frequency data; data convergence combines the mean and multi-data parallel analysis process to achieve a good balance… More >

  • Open Access

    ARTICLE

    Based on Compressed Sensing of Orthogonal Matching Pursuit Algorithm Image Recovery

    Caifeng Cheng1,2, Deshu Lin3,*
    Journal on Internet of Things, Vol.2, No.1, pp. 37-45, 2020, DOI:10.32604/jiot.2020.09116
    Abstract Compressive sensing theory mainly includes the sparsely of signal processing, the structure of the measurement matrix and reconstruction algorithm. Reconstruction algorithm is the core content of CS theory, that is, through the low dimensional sparse signal recovers the original signal accurately. This thesis based on the theory of CS to study further on seismic data reconstruction algorithm. We select orthogonal matching pursuit algorithm as a base reconstruction algorithm. Then do the specific research for the implementation principle, the structure of the algorithm of AOMP and make the signal simulation at the same time. In view of the OMP algorithm reconstruction… More >

  • Open Access

    ARTICLE

    Image Reconstruction Based on Compressed Sensing Measurement Matrix Optimization Method

    Caifeng Cheng1,2, Deshu Lin3,*
    Journal on Internet of Things, Vol.2, No.1, pp. 47-54, 2020, DOI:10.32604/jiot.2020.09117
    Abstract In this paper, the observation matrix and reconstruction algorithm of compressed sensing sampling theorem are studied. The advantages and disadvantages of greedy reconstruction algorithm are analyzed. The disadvantages of signal sparsely are preset in this algorithm. The sparsely adaptive estimation algorithm is proposed. The compressed sampling matching tracking algorithm supports the set selection and culling atomic standards to improve. The sparse step size adaptive compressed sampling matching tracking algorithm is proposed. The improved algorithm selects the sparsely as the step size to select the support set atom, and the maximum correlation value. Half of the threshold culling algorithm supports the… More >

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