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论文类型：期刊论文

发表时间：2020-02-01

发表刊物：JOURNAL OF MATHEMATICAL IMAGING AND VISION

收录刊物：EI、SCIE

卷号：62

期号：2

页面范围：253-271

ISSN号：0924-9907

关键字：Structure-preserving image filtering; Distance transform; Gestalt principles of grouping

摘要：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.