Many existing non-local means (NLM) methods either use Euclidean distance to measure the similarity between patches, or compute weight ωij only once and keep it unchanged during the subsequent denoising iterations, or use only the structure information of the denoised image to update weight ωij . These may lead to the limited denoising performance. To address these issues, this paper proposes the non-local adaptive means (NLAM) for image denoising. NLAM treats weight ωij as an optimization variable and iteratively updates its value. We then introduce three unbiased distances, namely, pixel-pixel, patch-patch, and coupled unbiased distances. These unbiased distances are more robust to measure the image pixel/patch similarity than Euclidean distance. Using the coupled unbiased distance, we propose the unbiased distance non-local adaptive means (UD-NLAM). Because UD-NLAM uses only a single patch size to compute weight ωij , we introduce multipatch UD-NLAM (MUD-NLAM) to adapt different noise levels. To further improve denoising performance, we then propose a new denoising method called MUD-NLAM with wavelet shrinkage (MUD-NLAM-WS). Experimental results show that the proposed NLAM, UD-NLAM, and MUD-NLAM outperform existing NLM methods, and MUD-NLAM-WS achieves a better performance than the state-of-the-art denoising methods.

中文翻译：

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具有小波收缩的Multipatch无偏距离非局部自适应均值。

许多现有的非局部均值（NLM）方法或者使用欧几里德距离来测量小块之间的相似度，或者仅计算一次权重ωij并在随后的去噪迭代中将其保持不变，或者仅使用去噪图像的结构信息来更新权重ωij。这些可能会导致降噪性能受到限制。为了解决这些问题，本文提出了一种用于图像去噪的非局部自适应方法。NLAM将权重ωij视为优化变量，并迭代更新其值。然后，我们介绍三个无偏距离，即像素-像素距离，面片-补丁距离和耦合无偏距离。这些无偏距离比欧几里得距离更能可靠地测量图像像素/面片相似度。使用耦合的无偏距离，我们提出了无偏距离非局部自适应方法（UD-NLAM）。由于UD-NLAM仅使用单个补丁大小来计算权重ωij，因此我们引入了多补丁UD-NLAM（MUD-NLAM）以适应不同的噪声水平。为了进一步提高去噪性能，我们然后提出了一种新的去噪方法，称为MUD-NLAM小波收缩（MUD-NLAM-WS）。实验结果表明，所提出的NLAM，UD-NLAM和MUD-NLAM优于现有的NLM方法，并且MUD-NLAM-WS的性能优于最新的降噪方法。