Structure-preserving image filtering is an image smoothing technique that aims to preserve prominent structures while removing unwanted details in natural images. However, relevant studies mainly focus on small variances/fluctuations suppression and are vulnerable to separate pixels connected by some low-contrast edges or cluster pixels which exhibit strong differences between neighbors in highly textured region. Inspired by the fact that the human visual system significantly outperforms manually designed operators in extracting meaningful structures from natural scenes, we present an efficient structure-preserving filtering method which integrates similarity, proximity and continuation principles of human perception to accomplish high-contrast details (textures/noises) smoothing. Additionally, a Liebig’s law of minimum-based distance transform is presented to seamlessly incorporate the three properties for the description of the filter kernel. Experiments demonstrate that our distance transform keeps a clustering-like manner of separating different image pixels and grouping similar ones with the awareness of structure. When integrating this affinity measure into the bilateral-filter-like framework, our method can efficiently remove high-contrast textures/noises while preserving major structures.

中文翻译：

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基于路径的结构保留图像滤波分析

保留结构的图像过滤是一种图像平滑技术，旨在保留突出的结构，同时去除自然图像中不需要的细节。然而，相关研究主要集中在小的方差/波动抑制上，并且容易受到由一些低对比度边缘或簇像素连接的独立像素的影响，这些像素在高度纹理化区域中的邻居之间表现出很大的差异。受人类视觉系统在从自然场景提取有意义的结构方面明显胜过人工设计的操作员的启发，我们提出了一种有效的结构保留过滤方法，该方法结合了人类感知的相似性，接近性和延续性原理来完成高对比度细节（纹理/噪声）平滑。另外，提出了基于最小距离的李比希定律，以无缝结合三个属性来描述滤波器内核。实验表明，我们的距离变换在保持结构意识的情况下，保持了类聚的方式来分离不同的图像像素并将相似的像素分组。当将这种亲和力度量整合到类似双边过滤器的框架中时，我们的方法可以在保留主要结构的同时有效去除高对比度的纹理/噪声。