Universität Des Saarlandes Fachrichtung 6.1 – Mathematik Generalised Nonlocal Image Smoothing Generalised Nonlocal Image Smoothing
Universität Des Saarlandes Fachrichtung 6.1 – Mathematik Generalised Nonlocal Image Smoothing Generalised Nonlocal Image Smoothing
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影响因子：
3.3
通讯作者：
L. Pizarro;P. Mrázek;S. Didas;Sven Grewenig;J. Weickert
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作者：
L. Pizarro;P. Mrázek;S. Didas;Sven Grewenig;J. Weickert
我们提出了一种由非局部数据和光滑度约束组成的图像平滑的离散变分方法，该约束约束惩罚了定义在图像块上的一般相异度量。这种不同的度量之一是斑块之间的加权距离。在这种情况下，我们推导了一个迭代邻域过滤器，它在光度域中引入了一个新的相似性度量。它可以看作是一种扩展的斑块相似性度量，它不仅评价两个选定像素的斑块相似度，而且还评价其对应邻域的相似度。这导致了更稳健的平滑过程，因为被选择用于平均的像素与局部图像结构更一致。建议的方法包括两个最近提出的过滤器作为特例：Buade等人的NL-Means过滤器。以及Mrázek等人的NDS过滤器。事实上，这里介绍的方法可以被认为是后一种过滤器的推广。我们评估了我们的方法去噪被高斯和脉冲噪声降级的灰度和彩色图像的任务，表明它比其他更复杂的基于补丁的方法要好得多。
We propose a discrete variational approach for image smoothing consisting of nonlocal data and smoothness contraints that penalise general dissimilarity measures defined on image patches. One of such dissimilarity measures is the weighted í µí°¿ 2 distance between patches. In such a case we derive an iterative neighbourhood filter that induces a new similarity measure in the photometric domain. It can be regarded as an extended patch similarity measure that evaluates not only the patch similarity of two chosen pixels, but also the similarity of their corresponding neighbours. This leads to a more robust smoothing process since the pixels selected for averaging are more coherent with the local image structure. The suggested approach includes two recently proposed filters as special cases: The NL-means filter of Buades et al. and the NDS filter of Mrázek et al. In fact, the approach introduced here can be considered as a generalisation of the latter filter. We evaluate our method for the task of denoising greyscale and colour images degraded by Gaussian and impulse noise, demonstrating that it compares very well to other more sophisticated patch-based approaches.