Collaborative filters perform denoising through transform-domain shrinkage of a group of similar patches extracted from an image. Existing collaborative filters of stationary correlated noise have all used simple approximations of the transform noise power spectrum adopted from methods which do not employ patch grouping and instead operate on a single patch. We note the inaccuracies of these approximations and introduce a method for the exact computation of the noise power spectrum. Unlike earlier methods, the calculated noise variances are exact even when noise in one patch is correlated with noise in any of the other patches. We discuss the adoption of the exact noise power spectrum within shrinkage, in similarity testing (patch matching), and in aggregation. We also introduce effective approximations of the spectrum for faster computation. Extensive experiments support the proposed method over earlier crude approximations used by image denoising filters such as Block-Matching and 3D-filtering (BM3D), demonstrating dramatic improvement in many challenging conditions.

中文翻译：

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相关噪声的协同过滤：精确的变换域方差，可改善收缩率和补丁匹配。

协作过滤器通过对从图像中提取的一组相似色块进行变换域收缩来执行去噪。现有的平稳相关噪声协作滤波器都使用了变换噪声功率谱的简单近似值，这些方法是从不采用补丁分组而只对单个补丁进行操作的方法中采用的。我们注意到这些近似的不准确性，并介绍了一种精确计算噪声功率谱的方法。与以前的方法不同，即使一个补丁中的噪声与任何其他补丁中的噪声相关联，所计算的噪声方差也是精确的。我们讨论在收缩，相似性测试（补丁匹配）和聚合中采用确切的噪声功率谱。我们还介绍了有效的频谱近似值，可加快计算速度。