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Image Enhancement Based on Pulse Coupled Neural Network in the Nonsubsample Shearlet Transform Domain

机译:基于脉冲耦合神经网络在非脉冲Shearlet变换域的图像增强

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摘要

In this study, pulse coupled neural network (PCNN) was modified and applied to the enhancement of blur images. In the transform domain of nonsubsample shearlet transform (NSST), PCNN was used to enhance the details of images in the low- and high-frequency subbands, and then the enhanced low- and high-frequency coefficients were used for NSST inverse transformation to obtain the enhanced images. The results showed that the proposed method can produce higher-quality images and suppress noise better than traditional image enhancement strategies.
机译:在该研究中,修改脉冲耦合神经网络(PCNN)并应用于模糊图像的增强。在Nonsubsample Shearlet变换(NSST)的变换域中,PCNN用于增强低频和高频子带中的图像的细节,然后增强的低频系数用于NSST逆变换以获得增强的图像。结果表明,该方法可以产生更高质量的图像,并比传统的图像增强策略更好地抑制噪声。

著录项

  • 来源
    《Mathematical Problems in Engineering》 |2019年第5期|2641516.1-2641516.11|共11页
  • 作者

    Qu Zhi; Xing Yaqiong; Song Yafei;

  • 作者单位

    Univ Sci & Technol China Hefei 230022 Anhui Peoples R China;

    Northwest Univ Sch Informat Sci & Technol Xian 710069 Shaanxi Peoples R China;

    Air Force Engn Univ China Xian 710051 Shaanxi Peoples R China;

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  • 正文语种 eng
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