Nonlocal means (NLM) is one of the classic patch-based methods for random noise attenuation. It assumes that a significant amount of redundant information exists in similar patches, which can be used to restore the original data. However, this method is computationally expensive due to a large number of overlapping patches. In addition, because this method uses a weighted average of patches to suppress noise, when applied to the data with complicated structures, the “average effect” may appear in the denoised results. We have implemented the NLM in the frequency-space domain, which can be called adaptive frequency-domain nonlocal means. This novel strategy will significantly reduce the computational cost and improve the quality of the final result compared to traditional NLM. Considering the impact of filtering parameters on the final result, we also build a mapping relationship between the noise strength and the filtering parameters, which could help obtain the denoised result with less signal leakage. We provide synthetic and field examples to show the superiority of this method over the traditional f-x prediction and NLM methods.

中文翻译：

---

地震随机噪声衰减的自适应频域非局部均值

非本地均值（NLM）是用于随机噪声衰减的经典基于补丁的方法之一。假定相似补丁中存在大量冗余信息，这些信息可用于还原原始数据。然而，由于大量的重叠补丁，该方法在计算上是昂贵的。另外，由于该方法使用补丁的加权平均值来抑制噪声，因此当应用于具有复杂结构的数据时，“平均效果”可能会出现在去噪结果中。我们已经在频空间域中实现了NLM，可以将其称为自适应频域非局部均值。与传统的NLM相比，这种新颖的策略将显着降低计算成本并提高最终结果的质量。考虑到过滤参数对最终结果的影响，我们还建立了噪声强度与滤波参数之间的映射关系，这有助于以更少的信号泄漏获得降噪的结果。我们提供了综合的和现场的例子，以证明该方法优于传统方法。外汇预测和NLM方法。