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Micro-scale searching algorithm for high-resolution image matting

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Abstract

Natural image matting based on pixel pair optimization is commonly employed during image post-processing. However, obtaining high-quality alpha mattes for high-resolution images via existing image matting methods is challenging as it typically requires considerable computational resources. In this paper, we design a novel optimization information transmission strategy that can be applied to images of different resolutions to improve the quality of the transmitted information required for evolutionary optimization. In addition, we propose a micro-scale searching matting algorithm, which allows us to obtain high-quality matting for high-resolution images with limited computational resources. To verify the applicability of the proposed algorithm for high-resolution images, experiments were conducted on the alpha matting benchmark dataset. Experimental results show that the proposed micro-scale searching matting algorithm can estimate high-quality alpha mattes without incurring excessive computational resources. Moreover, the proposed algorithm outperforms the state-of-the-art optimized matting algorithms when applied to high-resolution images.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported in part by the Guizhou Provincial Science and Technology Projects (QKHJCZK2022YB195, QKHJCZK2023YB143, QKHPTRCZCKJ2021007), in part by the Youth Science and Technology Talent Growth Project of Guizhou Province (QJHKY2021104), in part by the Natural Science Research Project of Education Department of Guizhou Province (QJJ2023061, QJJ2023012, QJJ2022015), in part by the Open Project of Key Laboratory of Pattern Recognition and Intelligent System of Guizhou Province (GZMUKL[2022]KF01), the National Natural Science Foundation of China under Grant 62002053, 62276103, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2019A1515111082 and 2020A1515110504, in part by the Natural Science Foundation of Guangdong Province under Grant 2021A1515011866, 2020A1515010696 and 2022A1515011491, in part by the Natural Science Foundation of Sichuan Province under Grant 2022YFG0314, in part by the the Guangdong University Key Platforms and Research Projects under Grant 2018KZDXM066, in part by the Key Research and Development Program of Zhongshan under Grant 2019A4018, in part by the the Science and Technology Foundation of Guangdong Province under Grant 2021A0101180005, in part by the the Major Science and Technology Foundation of Zhongshan City under Grant 2019B2009, 2019A40027 and 2021A1003, in part by the Zhongshan Science and Technology Research Project of Social welfare under Grant 210714094038458 and 2020B2017.

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Authors and Affiliations

  1. Guizhou Key Laboratory of Pattern Recognition and Intelligent System, Guizhou Minzu University, Guiyang, 550025, China

    Fujian Feng, Hongshan Gou, Le Feng, Mian Tan & Lin Wang

  2. School of Computer Science, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan, 528400, China

    Yihui Liang

  3. School of Software Engineering, South China University of Technology, Guangzhou, 510006, China

    Han Huang

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  1. Fujian Feng
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  2. Hongshan Gou
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  4. Le Feng
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  6. Han Huang
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  7. Lin Wang
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Correspondence to Hongshan Gou.

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Feng, F., Gou, H., Liang, Y. et al. Micro-scale searching algorithm for high-resolution image matting. Multimed Tools Appl 83, 38931–38947 (2024). https://doi.org/10.1007/s11042-023-17157-0

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