Chen Yiping, Wang Cheng, Wang Liansheng
J Xray Sci Technol. 2016 Mar 17;24(3):477-87. doi: 10.3233/XST-160564.
Non-local means algorithm can remove image noise in a unique way that is contrary to traditional techniques. This is because it not only smooths the image but it also preserves the information details of the image. However, this method suffers from high computational complexity. We propose a multi-scale non-local means method in which adaptive multi-scale technique is implemented. In practice, based on each selected scale, the input image is divided into small blocks. Then, we remove the noise in the given pixel by using only one block. This can overcome the low efficiency problem caused by the original non-local means method. Our proposed method also benefits from the local average gradient orientation. In order to perform evaluation, we compared the processed images based on our technique with the ones by the original and the improved non-local means denoising method. Extensive experiments are conducted and results shows that our method is faster than the original and the improved non-local means method. It is also proven that our implemented method is robust enough to remove noise in the application of neuroimaging.
非局部均值算法能够以一种有别于传统技术的独特方式去除图像噪声。这是因为它不仅能使图像平滑，还能保留图像的信息细节。然而，该方法存在计算复杂度高的问题。我们提出了一种实现了自适应多尺度技术的多尺度非局部均值方法。在实际操作中，基于每个选定的尺度，将输入图像划分为小的图像块。然后，仅使用一个图像块来去除给定像素中的噪声。这能够克服原始非局部均值方法所导致的效率低下问题。我们提出的方法还得益于局部平均梯度方向。为了进行评估，我们将基于我们技术处理后的图像与原始和改进的非局部均值去噪方法处理后的图像进行了比较。进行了大量实验，结果表明我们的方法比原始和改进的非局部均值方法更快。还证明了我们实现的方法在神经成像应用中具有足够的鲁棒性来去除噪声。
