欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
针对超声图像中的斑点噪声抑制问题，分析了经典的NL-MEANS算法去噪，提出了一种改进的算法——基于K均值聚类的NL-MEANS算法。通过引入聚类化的思想先将图像中的信息合理分类，使得分类信息具有较高的相似度，类间具有较低的相似度，利用NL-MEANS算法对分类后的图像进行去噪处理。改进算法抑制了斑点噪声，消除了传统NL-MEANS算法产生的人工伪影，保持了图像边缘和纹理信息的清晰度，实验结果表明了改进算法的有效性。
英文摘要：
According to suppress speckle noise in ultrasound images problem, the classic NL_ MEANS denoising algorithm is analyzed, and an improved algorithm-NL MEANS algorithm based on k-means clustering is proposed. By introducing the idea of clustering, the image information reasonably is classified, makes the class information have high similarity, have relatively low similarity between classes, NL_ MEANS algorithm is reused to denoise image classified. Algorithm is improved not only sup- press the speckle noise, but also eliminates the artificial artifacts of the traditional NL_ MEANS algorithm, and keeps the sharp- ness of the image edge and texture information, the experimental results verify the effectiveness of the improved algorithm.
同期刊论文项目
同项目期刊论文
期刊信息
位置：