降噪
希尔伯特-黄变换
非本地手段
水准点(测量)
计算机科学
多分辨率分析
小波
人工智能
分解
视频去噪
滤波器(信号处理)
维纳滤波器
小波变换
图像去噪
模式识别(心理学)
算法
计算机视觉
小波包分解
生物
多视点视频编码
生态学
大地测量学
视频跟踪
地理
对象(语法)
作者
Hana Rabbouch,Foued Saâdaoui,Hela Ibrahim,Mounir Zrigui
标识
DOI:10.1109/codit55151.2022.9804037
摘要
With the rapid evolution of computing and data storage technology, medical image denoising techniques have undergone significant improvement in recent years. Much of this success is in fact due to the emergence of multiresolution analysis (MRA) on both mathematical and algorithmic levels. In this article, we propose a hybrid multiresolution denoising approach coupling the two filters Non-Local Means and Wiener as well as a multiscale decomposition approach. A comparative study is carried out between two among the best-known MRA-based decomposition techniques: empirical mode decomposition (EMD) and empirical wavelet transform (EWT). Simulations in a denoising framework of a sample of benchmark X-ray images prove the effectiveness of multiscale denoising, especially when hybrid filtering is coupled to EWT. These results give several signs of their ability to be integrated into real-use scanners in the next few years.
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