Home / Journals / JNM / Vol.4, No.3, 2022
  • Open Access

    ARTICLE

    Research on Image Quality Enhancement Algorithm Using Hessian Matrix

    Xi Chen1, Yanpeng Wu2,*, Chenxue Zhu2, Hongjun Liu3
    Journal of New Media, Vol.4, No.3, pp. 117-123, 2022, DOI:10.32604/jnm.2022.027060
    Abstract The Hessian matrix has a wide range of applications in image processing, such as edge detection, feature point detection, etc. This paper proposes an image enhancement algorithm based on the Hessian matrix. First, the Hessian matrix is obtained by convolving the derivative of the Gaussian function. Then use the Hessian matrix to enhance the linear structure in the image. Experimental results show that the method proposed in this paper has strong robustness and accuracy. More >

  • Open Access

    ARTICLE

    No-Reference Stereo Image Quality Assessment Based on Transfer Learning

    Lixiu Wu1,*, Song Wang2, Qingbing Sang3
    Journal of New Media, Vol.4, No.3, pp. 125-135, 2022, DOI:10.32604/jnm.2022.027199
    Abstract In order to apply the deep learning to the stereo image quality evaluation, two problems need to be solved: The first one is that we have a bit of training samples, another is how to input the dimensional image’s left view or right view. In this paper, we transfer the 2D image quality evaluation model to the stereo image quality evaluation, and this method solves the first problem; use the method of principal component analysis is used to fuse the left and right views into an input image in order to solve the second problem. At the same time, the… More >

  • Open Access

    ARTICLE

    Menu Text Recognition of Few-shot Learning

    Xiaoyu1,2, Tian Zhenzhen2, Xin Zihao2, Liu Suolan2, Chen Fuhua3, Wang Hongyuan2,*
    Journal of New Media, Vol.4, No.3, pp. 137-143, 2022, DOI:10.32604/jnm.2022.027890
    Abstract Recent advances in OCR show that end-to-end (E2E) training pipelines including detection and identification can achieve the best results. However, many existing methods usually focus on case insensitive English characters. In this paper, we apply an E2E approach, the multiplex multilingual mask TextSpotter, which performs script recognition at the word level and uses different recognition headers to process different scripts while maintaining uniform loss, thus optimizing script recognition and multiple recognition headers simultaneously. Experiments show that this method is superior to the single-head model with similar number of parameters in end-to-end identification tasks. More >

  • Open Access

    ARTICLE

    Microphone Array-Based Sound Source Localization Using Convolutional Residual Network

    Ziyi Wang1, Xiaoyan Zhao1,*, Hongjun Rong1, Ying Tong1, Jingang Shi2
    Journal of New Media, Vol.4, No.3, pp. 145-153, 2022, DOI:10.32604/jnm.2022.030178
    Abstract Microphone array-based sound source localization (SSL) is widely used in a variety of occasions such as video conferencing, robotic hearing, speech enhancement, speech recognition and so on. The traditional SSL methods cannot achieve satisfactory performance in adverse noisy and reverberant environments. In order to improve localization performance, a novel SSL algorithm using convolutional residual network (CRN) is proposed in this paper. The spatial features including time difference of arrivals (TDOAs) between microphone pairs and steered response power-phase transform (SRP-PHAT) spatial spectrum are extracted in each Gammatone sub-band. The spatial features of different sub-bands with a frame are combine into a… More >

  • Open Access

    ARTICLE

    Semi-Supervised Medical Image Segmentation Based on Generative Adversarial Network

    Yun Tan1,2, Weizhao Wu2, Ling Tan3, Haikuo Peng2, Jiaohua Qin2,*
    Journal of New Media, Vol.4, No.3, pp. 155-164, 2022, DOI:10.32604/jnm.2022.031113
    Abstract At present, segmentation for medical image is mainly based on fully supervised model training, which consumes a lot of time and labor for dataset labeling. To address this issue, we propose a semi-supervised medical image segmentation model based on a generative adversarial network framework for automated segmentation of arteries. The network is mainly composed of two parts: a segmentation network for medical image segmentation and a discriminant network for evaluating segmentation results. In the initial stage of network training, a fully supervised training method is adopted to make the segmentation network and the discrimination network have certain segmentation and discrimination… More >

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