• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>2014 International Conference on Advances in Engineering and Technology Research >An efficient curvelet Bayesian Network based approach for image denoising
【24h】

An efficient curvelet Bayesian Network based approach for image denoising

机译：基于有效曲波贝叶斯网络的图像去噪方法

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The development in the processing capabilities of electronic devices directed the research of efficient image denoising technique towards the more complex methods which utilizes the complex transforms, functional analysis and statistics. Even though with the sophistication of the recently developed techniques, most algorithms fails to achieve desirable level of performance. Most algorithm fails because the practical model does not matches the algorithm assumptions taken at the time of development. This paper presents an efficient approach for the image denoising based on curvelet transform and the Bayesian Network. The proposed technique utilizes the statistical dependencies in the curvelet domain to train the Bayesian Network which is then used for predicting the noise probability. The curvelet transform provides better approximation especially in directional discontinuities which makes it preferable for processing the pixels around the edges. The experimental results show that the proposed technique outperforms wavelet based methods visually and mathematically (in terms of peak signal-to-noise ratio (PSNR)).
机译：电子设备处理能力的发展将有效的图像去噪技术的研究引向了利用复杂的变换，功能分析和统计的更加复杂的方法。即使采用了最近开发的技术，大多数算法仍无法达到理想的性能水平。大多数算法失败，因为实际模型与开发时采用的算法假设不符。本文提出了一种基于Curvelet变换和贝叶斯网络的有效去噪方法。所提出的技术利用曲线波域中的统计依赖性来训练贝叶斯网络，然后将贝叶斯网络用于预测噪声概率。 Curvelet变换可提供更好的近似效果，尤其是在方向不连续的情况下，这使其更适合处理边缘周围的像素。实验结果表明，所提出的技术在视觉和数学上都优于基于小波的方法（就峰值信噪比（PSNR）而言）。

著录项

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