Image denoising by arithmetic means based on similarity
Image denoising by arithmetic means based on similarity
复制标题
DOI：
10.1109/icics.2015.7459953
复制
发表时间：
2015-12
期刊：
影响因子：
--
通讯作者：
Yutaka Takagi;M. Ikehara
中科院分区：
文献类型：
--
作者：
Yutaka Takagi;M. Ikehara
在本文中，我们提出了一种基于非局部均值算法的去噪方法。在传统的NLM中，基于目标块与其相邻块之间的相似度来获取权重函数，然后基于相似度来计算高斯范围核。然后，目标补丁被相邻补丁的加权平均值替换。相比之下，我们的方法通过阈值提取相似的补丁，并且只计算简单的算术平均值。该方法不仅优于传统的 NLM，而且实现时计算量更少。最后，我们将所提出的 NLM 与传统的 NLM 进行比较，并验证其优势。
In this paper, we propose a Non-Local Means algorithm-based denoising method. In conventional NLM, the weighting functions are acquired based on the similarity between target patch and its neighboring patches and then Gaussian-range kernel is calculated based on the similarity. Then, target patch is replaced by weighted means value of neighboring patches. In comparison, our method extracts similar patches by thresholding and only calculates simple arithmetic average. The method does not only outperform the conventional NLM but also implement with less computation. Finally, we compare the proposed and the conventional NLM, and validate the advantage.