.png){kind=icon}
返回
Echocardiography noise reduction using sparse representation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compeleceng.2015.12.008.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The clarity and accuracy of echocardiography images are greatly reduced by speckle noise. Noise suppression, however, is difficult to achieve without also obscuring both rapidly moving structures and object edges. This research seeks to address these challenges by introducing a novel filtering framework based on temporal information and sparse representation. The proposed method involves smoothing intensity variation time curves (IVTCs) assessed in each pixel. Using an over-complete dictionary that contains prototype signal atoms, IVTCs can be reconstructed as linear combinations of a few of these atoms. After a comprehensive comparison of sparse recovery algorithms, three were selected for our method: Bayesian Compressive Sensing (BCS), the Bregman iterative algorithm, and Orthogonal Matching Pursuit (OMP). The performance of the proposed method was then evaluated and compared with other speckle reduction filters. The experimental results demonstrate that the proposed algorithm can be used to achieve better-preserved edges and reduce blurring. (C) 2015 Elsevier Ltd. All rights reserved.
Keyword:
Echocardiographic images
Noise reduction
Temporal information
Sparse representation
Adaptive thresholding
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
C
IF:
4.9
论文数:
6.7K
被引数:
1.3W
机构
引用论文
.png){kind=icon}
A new TwIST: Two-step iterative shrinkage/thresholding algorithms for image restoration一种新的扭曲: 用于图像恢复的两步迭代收缩/阈值算法
PREAI
分享收藏
查看更多内容