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Path-Based Analysis for Structure-Preserving Image Filtering

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Abstract

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.

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  1. https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/.

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Acknowledgements

This research was supported by the National Key Research and Development Program of China (No.2018YFA0704605), the National Key Project of Science and Technology of China (No.2017ZX05064), National Natural Science Foundation of China (No. 61272523) and China Scholarship Council (CSC).

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Authors and Affiliations

  1. School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China

    Lijuan Xu, Fan Wang, Qi Wang, Yan Yang & Xiaopeng Hu

  2. Department of Engineering, University of Vic-Central University of Catalonia, 08500, Vic, Barcelona, Spain

    Laura Dempere-Marco

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  1. Lijuan Xu
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Correspondence to Xiaopeng Hu.

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Xu, L., Wang, F., Dempere-Marco, L. et al. Path-Based Analysis for Structure-Preserving Image Filtering. J Math Imaging Vis 62, 253–271 (2020). https://doi.org/10.1007/s10851-019-00941-9

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