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Edge-aware texture filtering with superpixels constraint

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Abstract

Extracting meaningful structural edges from complex texture images presents a significant challenge. Accurately measuring and differentiating texture information within an image are crucial for efficient texture filtering. While most existing texture filtering methods employ regular rectangular filter windows, the irregularity inherent in textures and structures can limit measurement accuracy, reducing the effectiveness of texture filtering. To address this problem, we propose an edge-aware texture filtering method that integrates superpixels. By employing patch shift, our filter constructs an edge-aware filtering region with superpixels constraint. This region includes pixels with minimal differences and similar texture characteristics. Utilizing the perceptual properties of superpixels for irregular edges enhances texture measurement, thereby improving the quality of texture filtering. Experimental results demonstrate that the proposed method outperforms existing techniques, yielding superior filtering outcomes. The source code is available at: https://github.com/kxZhang1016/EATFS.

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Data Availability

The datasets used in this paper are public datasets.

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Acknowledgements

This work was supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202201148), the Humanities and Social Sciences Research Program of Chongqing Municipal Education Commission (Grant No. 23SKGH263), the Foundation and Frontier Research Key Program of Chongqing Science and Technology Commission (Grant No. cstc2015jcyjBX0127), the National Natural Science Foundation of China for Young Scientists (Grant No. 61502065), and the Funding Achievements of the Action Plan for High Quality Development of Graduate Education at Chongqing University of Technology (Grant No. gzlcx20233225).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, 400054, China

    Jianwu Long & Kaixin Zhang

  2. College of Computer Science and Technology, Jilin University, Jilin, 130012, China

    Jiangzhou Zhu

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  1. Jianwu Long
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  2. Kaixin Zhang
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Contributions

All authors contributed to the study design. J.L. was involved in conceptualization, resources, and writing—reviewing and editing. K.Z. was responsible for methodology, software, validation, and writing—original draft. J.Z. contributed to data curation and visualization.

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Correspondence to Kaixin Zhang.

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Long, J., Zhang, K. & Zhu, J. Edge-aware texture filtering with superpixels constraint. Vis Comput (2024). https://doi.org/10.1007/s00371-024-03415-1

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