首页> 外文期刊>Mathematical Problems in Engineering >Image Enhancement Based on Pulse Coupled Neural Network in the Nonsubsample Shearlet Transform Domain
【24h】

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

机译:非子样本Shearlet变换域中基于脉冲耦合神经网络的图像增强

获取原文
获取原文并翻译 | 示例

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:在这项研究中,修改了脉冲耦合神经网络(PCNN)并将其应用于模糊图像的增强。在非子样本小波变换(NSST)的变换域中,使用PCNN增强低频和高频子带中的图像细节,然后将增强的低频和高频系数用于NSST逆变换以获得增强图像。结果表明,与传统的图像增强策略相比,该方法可以产生更高质量的图像,并且可以更好地抑制噪声。

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;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号