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Edge guidance filtering for structure extraction

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Abstract

Smoothing the multiscale, irregular, and high contrast textures while maintaining structures with small details is challenging for the existing texture filtering methods. In this paper, we put forward a novel edge guidance-based texture filter with an adaptive kernel scale scheme to address these challenges. The texture edges are identified by a texture edge detector first. Then, based on the texture edges, a variable per-pixel smoothing scale is selected to construct the scale map, which is used to guide the filtering. In the end, a novel pixel-selected filter is designed as post-processing to optimize the filtered images. The experimental results compared with the state-of-the-art methods show that our method has a better performance in suppressing different forms of textures while maintaining the main structure. In addition, our method can be applied well in a variety of image processing applications including: detail enhancement, inverse halftoning, virtual contour restoration and texture image segmentation.

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References

  1. Kou, F., Wei, Z., Chen, W., Wu, X., Wen, C., Li, Z.: Intelligent detail enhancement for exposure fusion. IEEE Trans. Multimed. 20(2), 484–495 (2017)

    Article  Google Scholar 

  2. Zhou, Z., Wang, B., Ma, J.: Scale-aware edge-preserving image filtering via iterative global optimization. IEEE Trans. Multimed. 20(6), 1392–1405 (2017)

    Article  Google Scholar 

  3. Şener, O., Ugur, K., Alatan, A.A.: Efficient mrf energy propagation for video segmentation via bilateral filters. IEEE Trans. Multimed. 16(5), 1292–1302 (2014)

    Article  Google Scholar 

  4. Xu, L., Yan, Q., Xia, Y., Jia, J.: Structure extraction from texture via relative total variation. ACM Trans. Graph. (TOG) 31(6), 139 (2012)

    Article  Google Scholar 

  5. Cho, H., Lee, H., Kang, H., Lee, S.: Bilateral texture filtering. ACM Trans. Graph. (TOG) 33(4), 128 (2014)

    Article  Google Scholar 

  6. Zhang, C., Ge, L., Chen, Z., Li, M., Liu, W., Chen, H.: Refined tv-l1 optical flow estimation using joint filtering. IEEE Trans. Multimed. 22(2), 349–364 (2019)

    Article  Google Scholar 

  7. Gao, Y., Hu, H.M., Li, B., Guo, Q.: Naturalness preserved nonuniform illumination estimation for image enhancement based on retinex. IEEE Trans. Multimed. 20(2), 335–344 (2017)

    Article  Google Scholar 

  8. Ghosh, S., Gavaskar, R.G., Panda, D., Chaudhury, K.N.: Fast scale-adaptive bilateral texture smoothing. IEEE Trans. Circuits Syst. Video Technol. 30(7), 2015–2026 (2020)

    Google Scholar 

  9. He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2012)

    Article  Google Scholar 

  10. Farbman, Z., Fattal, R., Lischinski, D., Szeliski, R.: Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Trans. Graph. (TOG) ACM 27, 67 (2008)

    Google Scholar 

  11. Chen, B., Jung, C., Zhang, Z.: Variational fusion of time-of-flight and stereo data for depth estimation using edge-selective joint filtering. IEEE Trans. Multimed. 20(11), 2882–2890 (2018)

    Article  Google Scholar 

  12. Xu, P., Wang, W.: Structure-aware window optimization for texture filtering. IEEE Trans. Image Process. 28(9), 4354–63 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  13. Deng, G.: Edge-aware bma filters. IEEE Trans. Image Process. 25(1), 439–454 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  14. Chen, X., Kang, S.B., Jie, Y., Yu, J.: Fast edge-aware denoising by approximated patch geodesic paths. IEEE Trans. Circuits Syst. Video Technol. 25(6), 897–909 (2015)

    Article  Google Scholar 

  15. Eun, H., Kim, C.: Superpixel-guided adaptive image smoothing. IEEE Signal Process. Lett. 23(12), 1887–1891 (2016)

    Article  Google Scholar 

  16. Li, Z., Zheng, J., Zhu, Z., Yao, W., Wu, S.: Weighted guided image filtering. IEEE Trans. Image Process. 24(1), 120–129 (2014)

    MathSciNet  MATH  Google Scholar 

  17. Lin, T.C.: A new adaptive center weighted median filter for suppressing impulsive noise in images. Inf. Sci. 177(4), 1073–1087 (2007)

    Article  Google Scholar 

  18. Liu, W., Zhang, P., Lei, Y., Huang, X., Yang, J., Reid, ID.: A generalized framework for edge-preserving and structure-preserving image smoothing. In: National Conference on Artificial Intelligence (2020)

  19. Liu, W., Zhang, P., Lei, Y., Huang, X., Yang, J., Ng, MKP.: A generalized framework for edge-preserving and structure-preserving image smoothing. IEEE Trans. Pattern Anal. Mach. Intell. pp 1 (2021)

  20. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. IEEE Int. Conf. Comput. Vision 98, 2 (1998)

    Google Scholar 

  21. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)

    Article  Google Scholar 

  22. Liu, W., Zhang, P., Huang, X., Yang, J., Shen, C., Reid, I.: Real-time image smoothing via iterative least squares. ACM Trans. Graph. 39(3), 1–24 (2020)

    Article  Google Scholar 

  23. Liu, W., Chen, X., Shen, C., Liu, Z., Yang, J.: Semi-global weighted least squares in image filtering. In: 2017 IEEE International Conference on Computer Vision (ICCV), IEEE Computer Society, pp 5862–5870 (2017)

  24. Liu, W., Zhang, P., Chen, X., Shen, C., Huang, X., Yang, J.: Embedding bilateral filter in least squares for efficient edge-preserving image smoothing. IEEE Trans. Circuits Syst. Video Technol. 30(1), 23–35 (2020)

    Article  Google Scholar 

  25. Karacan, L., Erdem, E., Erdem, A.: Structure-preserving image smoothing via region covariances. ACM Trans. Graph. (TOG) 32(6), 176 (2013)

    Article  Google Scholar 

  26. Zhang, Q., Shen, X., Xu, L., Jia, J.: Rolling guidance filter. In: European conference on computer vision, Springer, pp 815–830 (2014)

  27. Lin, T.H., Way, D.L., Shih, Z.C., Tai, W.K., Chang, C.C.: An efficient structure-aware bilateral texture filtering for image smoothing. Comput. Graph. Forum Wiley Online Libr. 35, 57–66 (2016)

    Article  Google Scholar 

  28. Jain, P., Tyagi, V.: An adaptive edge-preserving image denoising technique using tetrolet transforms. Vis. Comput. 31(5), 657–674 (2015)

    Article  Google Scholar 

  29. Bao, L., Song, Y., Yang, Q., Yuan, H., Wang, G.: Tree filtering: efficient structure-preserving smoothing with a minimum spanning tree. IEEE Trans. Image Process. 23(2), 555–569 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  30. Zhang, F., Dai, L., Xiang, S., Zhang, X.: Segment graph based image filtering: Fast structure-preserving smoothing. In: Proceedings of the IEEE International Conference on Computer Vision, pp 361–369 (2015)

  31. Yu, L.H., Feng, Y.Q., Chen, W.F.: Adaptive regularization method based total variational de-noising algorithm. J. Image Graph. 14(10), 1950–4 (2009)

    Google Scholar 

  32. Xu, L., Lu, C., Xu, Y., Jia, J.: Image smoothing via \({L}_0\) gradient minimization. ACM Trans. Graph. TOG ACM 30, 174 (2011)

    Google Scholar 

  33. Ham, B., Cho, M., Ponce, J.: Robust image filtering using joint static and dynamic guidance. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4823–4831 (2015)

  34. Li, Y., Huang, JB., Ahuja, N., Yang, MH.: Deep joint image filtering. In: European Conference on Computer Vision, Springer, pp 154–169 (2016)

  35. Kim, Y., Ham, B., Do, M.N., Sohn, K.: Structure-texture image decomposition using deep variational priors. IEEE Trans. Image Process. 28(6), 2692–2704 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  36. Zhu, F., Liang, Z., Jia, X., Zhang, L., Yu, Y.: A benchmark for edge-preserving image smoothing. IEEE Trans. Image Process. 28(7), 3556–3570 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  37. Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)

    Article  Google Scholar 

  38. Martin, D., Fowlkes, C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 530–549 (2004)

    Article  Google Scholar 

  39. Liu, Y., Liu, G., Liu, C., Sun, C.: A novel color-texture descriptor based on local histograms for image segmentation. IEEE Access 7(160), 160683–160695 (2019)

    Article  Google Scholar 

  40. Liu, Y., Liu, G., Liu, H., Liu, C.: Structure-aware texture filtering based on local histogram operator. IEEE Access 8, 43838–43849 (2020)

    Article  Google Scholar 

  41. Zhang, Z., He, H.: A customized low-rank prior model for structured cartoon-texture image decomposition. Signal Process. Image Commun. 96(8), 116308 (2021)

    Article  Google Scholar 

  42. Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  43. Su, Z., Luo, X., Deng, Z., Liang, Y., Ji, Z.: Edge-preserving texture suppression filter based on joint filtering schemes. IEEE Trans. Multimed. 15(3), 535–548 (2012)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by China’s national major scientific research instrument development project (42127807), Key project of Guangdong Province for Promoting High-quality Economic Development (Marine Economic Development) in 2022: Research and development of key technology and equipment for Marine vibroseis system (GDNRC[2022]29), Special fund for applied basic research of Changchun Science and Technology Department(21ZY21), Jilin Science and technology development plan, key R & D projects (20220201055GX), and Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang) under contract No.ZJW-2019-04.

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  1. Beichen Sun and Yang Liu have contributed equally to this work.

Authors and Affiliations

  1. College of Instrumentation and Electrical Engineering, Jilin University, Ximinzhu Avenue, Changchun, 130012, Jilin, China

    Beichen Sun, Guanyu Zhang & Yang Liu

  2. Department of Art and Science, Queen’s University, 99 University Avenue, Kingston, Ontario, K7L 3N6, Canada

    Yuehan Qi

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  1. Beichen Sun
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Correspondence to Yang Liu.

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Sun, B., Qi, Y., Zhang, G. et al. Edge guidance filtering for structure extraction. Vis Comput 39, 5327–5342 (2023). https://doi.org/10.1007/s00371-022-02662-4

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