带结构检测的非局部均值图像去噪算法

针对非局部均值(NL-Means)图像去噪算法有大量结构残留的问题，提出一种带结构检测的NL-Means滤波算法。首先使用一个结构分析器对噪声图像进行预处理，突出图像中的细节信息，然后利用边缘检测的结果调节NL-Means算法相似性度量，为了保留图像的边缘内容让具有相似边缘内容的像素能够获得更大的权，而边缘内容不相似邻域有较小的权（或为零）。实验结果表明：该算法提高了NL-Means算法的去噪能力，滤波后的图像结构相似度更高，改善了图像的视觉质量。

Enhanced non-local means image denoising algorithm using structure detection

Concerning the problem of residual structure on Non-Local Means (NL-Means) image denoising algorithm, an improved NL-Means image denoising algorithm with structure detection was proposed. The noisy image was first processed by using a structure analyzer to extract detail information before filtering, then the similarity measure was modified to incorporate the result of edge detection, more weight was given to a pixel if its edge content was more similar to that of the pixel being denoised and the neighborhood with a dissimilar edge pattern should receive a lower weight (or zero) to preserve the original edge content. The experimental results show that the proposed algorithm enhances NL-Means denoising ability and image structural similarity, and improves the visual quality of the denoised image.