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Hashed Nonlocal Means for Rapid Image Filtering

机译：快速的图像过滤的非本地化手段

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摘要

Denoising algorithms can alleviate the trade-off between noise-level and acquisition time that still exists for certain image types. Nonlocal means, a recently proposed technique, outperforms other methods in removing noise while retaining image structure, albeit at prohibitive computational cost. Modifications have been proposed to reduce the cost, but the method is still too slow for practical filtering of 3D images. This paper proposes a hashed approach to explicitly represent two summed frequency (hash) functions of local descriptors (patches), utilizing all available image data. Unlike other approaches, the hash spaces are discretized on a regular grid, so primarily linear operations are used. The large memory requirements are overcome by recursing the hash spaces. Additional speed gains are obtained by using a marginal linear interpolation method. Careful choice of the patch features results in high computational efficiency, at similar accuracies. The proposed approach can filter a 3D image in less than a minute versus 15 minutes to 3 hours for existing nonlocal means methods.
机译：去噪算法可以减轻某些图像类型仍然存在的噪声级和采集时间之间的折衷。非局部手段是一种最近提出的技术，尽管在计算成本上过高，但在保留图像结构的同时去除噪声方面优于其他方法。已经提出修改以降低成本，但是该方法对于实际的3D图像滤波仍然太慢。本文提出了一种散列方法，利用所有可用的图像数据来显式表示局部描述符（补丁）的两个求和频率（散列）函数。与其他方法不同，哈希空间在常规网格上离散化，因此主要使用线性运算。通过递归散列空间可以克服大内存需求。通过使用边际线性插值方法可以获得额外的速度增益。仔细选择补丁功能会以相似的精度获得较高的计算效率。所提出的方法可以在不到一分钟的时间内过滤3D图像，而对于现有的非局部均值方法则需要15分钟到3个小时。

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